Overfitting is a well-known issue in machine learning that occurs when a model struggles to generalize its predictions to new, unseen data beyond the scope of its training set. Traditional techniques to mitigate overfitting include early stopping, data augmentation, and regularization. In this work, we demonstrate that the degree of overfitting of a trained model is correlated with the ability to generate counterfactual examples. The higher the overfitting, the easier it will be to find a valid counterfactual example for a randomly chosen input data point. Therefore, we introduce CF-Reg, a novel regularization term in the training loss that controls overfitting by ensuring enough margin between each instance and its corresponding counterfactual. Experiments conducted across multiple datasets and models show that our counterfactual regularizer generally outperforms existing regularization techniques.

We introduce a load-aware selective embedding-pruning method to be used in dense retrieval systems. Our method adapts the embedding dimensionality at inference time based on per-query deadlines and the current queue occupancy. Under light load, full-dimensional embeddings maximise effectiveness; as load increases, dimensionality is adaptively reduced to maintain throughput and avoid query drops. Our approach is orthogonal to the specific dimensionality reduction strategy employed and operates independently of the embedding model. We evaluate our approach using both PCA-reduced embeddings and nested Matryoshka embeddings. Empirical results show that our load-aware strategy consistently achieves a better effectiveness–efficiency trade-off than static dimensionality reduction baselines across varying load conditions. As a by-product of this study, our experiments show that under selective pruning, the PCA-based approach consistently outperforms Matryoshka, indicating that specialised multi-representation training is not strictly required for robust load balancing in dense retrieval. Our code is available at: https://github.com/mariadianacalugaru/selective_pruning.

Recently, graph neural networks (GNNs) have become the new state-of-the-art approach to developing powerful recommender systems. However, it is hard for GNN-based recommender systems to attach tangible explanations of why a specific item ends up in the list of top- k suggestions for a given user. Indeed, explaining GNN-based recommendations is unique, and existing GNN explanation methods are inappropriate since they are designed to explain node, edge, or graph classification rather than ranking. In this work, we propose GREASE, a novel method for explaining the list of top- k suggested items to a given user provided by any black-box GNN-based recommender system. Specifically, for each recommended item, GREASE first trains a surrogate GNN model on the subgraph obtained as the union of the target user-item pair and its l -hop neighborhood. Then, it jointly generates factual and counterfactual explanations by finding optimal adjacency matrix perturbations to capture the sufficient and necessary conditions for the item to be recommended. Experiments on real-world datasets show that GREASE can generate concise and compelling explanations for popular GNN-based recommender models.

Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships. However, current GNN techniques represent nodes by means of a single static vector, which may inadequately capture the intricate complexities of users and items. To overcome these limitations, we propose a solution integrating a cutting-edge model inspired by category theory: Sheaf4Rec. Unlike single vector representations, Sheaf Neural Networks and their corresponding Laplacians represent each node (and edge) using a vector space. Our approach takes advantage of this theory and results in a more comprehensive representation that can be effectively exploited during inference, providing a versatile method applicable to a wide range of graph-related tasks and demonstrating unparalleled performance. Our proposed model exhibits a noteworthy relative improvement of up to 8.53% on F1-Score@10 and an impressive increase of up to 11.29% on NDCG@10, outperforming existing state-of-the-art models such as Neural Graph Collaborative Filtering (NGCF), KGTORe and other recently developed GNN-based models. In addition to its superior predictive capabilities, Sheaf4Rec shows remarkable improvements in terms of efficiency: we observe substantial runtime improvements ranging from 2.5% up to 37% when compared to other GNN-based competitor models, indicating a more efficient way of handling information while achieving better performance. Code is available at https://github.com/antoniopurificato/Sheaf4Rec .

This workshop will explore the latest approaches to medical knowledge systems, with a focus on the synergy between large language models, retrieval-augmented generation, and foundation/agentic models. The workshop will promote interdisciplinary collaboration among researchers, practitioners, and clinicians to advance evidence-driven AI in healthcare. Topics will include knowledge-grounded question answering, biomedical document retrieval, multimodal clinical reasoning, personalization, safety, and the challenges of deploying AI in practice. With a strong emphasis on reproducibility, evaluation, and responsible application in clinical settings, the workshop will define the next frontier of knowledge-centric AI in medicine.

Modeling human behavior through the lens of online social networks presents both a significant opportunity and a challenge for understanding complex social phenomena, such as misinformation spread, opinion formation and polarization. While agent-based models (ABMs) are widely used for studying these social phenomena, parameter estimation remains a challenge, often relying on costly simulation-based heuristics. This work uses variational inference to estimate the parameters of an opinion dynamics ABM by transforming the estimation problem into an optimization task that can be solved directly. Our proposal relies on probabilistic generative ABMs (PGABMs): we start by synthesizing a probabilistic generative model from the ABM rules. Then, we transform the inference process into an optimization problem suitable for automatic differentiation. In particular, we use the Gumbel-Softmax reparameterization for categorical agent attributes and Stochastic Variational Inference for parameter estimation. Moreover, we explore the trade-offs of using variational distributions with different complexities: Normal distributions and Normalizing Flows. We validate our method on a bounded confidence model with agent roles (leaders and followers), by estimating both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic (200 categorical agent-level roles) parameters more accurately than simulation-based and MCMC methods.

In recent years, two-sided marketplaces have emerged as viable business models in many real-world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing the supply and demand of a specific good. Examples of industries include but are not limited to accommodation (Airbnb, Booking.com), video content (YouTube, Instagram, TikTok), ridesharing (Uber, Lyft), online shops (Etsy, Ebay, Facebook Marketplace), music (Spotify, Amazon), app stores (Apple App Store, Google App Store) or job sites (LinkedIn). The traditional research in most of these industries focused on satisfying the demand. OTAs would sell hotel accommodation, TV networks would broadcast their own content, or taxi companies would own their own vehicle fleet. In modern examples like Airbnb, YouTube, Instagram, or Uber, the platforms operate by outsourcing the service they provide to their users, whether they are hosts, content creators or drivers, and have to develop their models considering their needs and goals.

Upselling recommendations play a critical role in improving customer engagement and maximizing revenue in the telecommunications industry. However, real-world data on such interactions often presents unique challenges, including multiple recommendations per call and sparse customer feedback, which complicates the evaluation of recommender systems. Our review of the existing literature reveals a critical gap in publicly available datasets that reflect these challenges, limiting progress in developing and evaluating upselling strategies.This work introduces a novel dataset that captures these complexities, offering valuable insights into customer behavior and recommendation effectiveness. The dataset, derived from real-world interactions between customers and service providers, contains multiple recommendations provided in individual calls and sparse feedback, reflecting typical user behavior where interest may be low or unrecorded.To aid in the development of more effective recommendation systems, we provide detailed statistics on recommendation distributions, user engagement, and feedback patterns. Furthermore, we benchmark various recommendation models, from classical approaches to state-of-the-art neural networks, allowing for a comprehensive assessment of their recommendation accuracy in this challenging setting.

In recent years, Retrieval-Augmented Generation (RAG) systems have become a cornerstone of artificial intelligence, attracting considerable attention in a variety of fields. By integrating the strengths of information retrieval and generative models, these systems have shown immense potential to push the boundaries of machine learning applications. Nevertheless, RAG systems still face significant challenges and offer ample room for advancement and innovation.

Dimension IMportance Estimation (DIME) is a recently proposed technique to enhance ranking effectiveness of dense retrieval models by pruning irrelevant embedding dimensions through Pseudo Relevance Feedback (PRF DIME) or exploiting dense representations of Large Language Model-generated answers (LLM DIME). Despite strong empirical performance, its theoretical foundations and generalizability remain open questions.

Recommender systems based on Graph Neural Networks (GNN) have become the state-of-the-art approach in recommendation, but they struggle with in extreme cold-start settings, where most users or items lack interaction data. This paper proposes a novel framework to address this challenge in four steps: (i) a propensity model to predict item purchase behaviour, with associated explainability to identify the most relevant features, (ii) a link augmentation module to connect users based on previously obtained similarities, (iii) a GNN-based link prediction step on the obtained dense graph and (iv) a final re-ranking stage to increase diversity in predictions leveraging users embeddings. By exploiting the enriched graph structure, the framework generates embeddings for cold-start users and items, enabling diverse recommendations, containing long tail and unsold items, for both established and new users. We validate the framework's effectiveness on real-world industrial data from TIM S.p.A.

Community detection techniques are useful for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often comes at the expense of potentially exposing individuals to privacy breaches by inadvertently revealing their tastes or preferences. Therefore, some users may wish to preserve their anonymity and opt out of community detection for various reasons, such as affiliation with political or religious organizations, without leaving the platform. In this study, we address the challenge of community membership hiding, which involves strategically altering the structural properties of a network graph to prevent one or more nodes from being identified by a given community detection algorithm. We tackle this problem by formulating it as a constrained counterfactual graph objective, and we solve it via deep reinforcement learning. Extensive experiments demonstrate that our method outperforms existing baselines, striking the best balance between accuracy and cost.

In recent years, two-sided marketplaces have emerged as viable business models in many real-world applications. In particular, we have moved from the social network paradigm to a network with two distinct types of participants representing the supply and demand of a specific good. Examples of industries include but are not limited to accommodation (Airbnb, Booking.com), video content (YouTube, Instagram, TikTok), ridesharing (Uber, Lyft), online shops (Etsy, Ebay, Facebook Marketplace), music (Spotify, Amazon), app stores (Apple App Store, Google App Store) or job sites (LinkedIn). The traditional research in most of these industries focused on satisfying the demand. OTAs would sell hotel accommodation, TV networks would broadcast their own content, or taxi companies would own their own vehicle fleet. In modern examples like Airbnb, YouTube, Instagram, or Uber, the platforms operate by outsourcing the service they provide to their users, whether they are hosts, content creators or drivers, and have to develop their models considering their needs and goals.

In recent years, recommender systems have become indispensable tools in various domains, aiding users in discovering relevant content amidst the overwhelming amount of available material. However, the effectiveness and reliability of these systems are often hindered by various challenges such as data perturbations, missing data, noise, and bias. In this workshop, we aim to explore and address these challenges by focusing on the development of robust recommender systems. Robustness in recommender systems refers to their ability to maintain performance and effectiveness under adverse conditions, including unexpected variations in the data environment. By fostering discussions and collaborations among researchers and practitioners, this workshop seeks to advance the state-of-the-art in robust recommender systems, thereby enhancing their usability and trustworthiness in real-world applications.

Retrieval-Augmented Generation (RAG) has recently emerged as a method to extend beyond the pre-trained knowledge of Large Language Models by augmenting the original prompt with relevant passages or documents retrieved by an Information Retrieval (IR) system. RAG has become increasingly important for Generative AI solutions, especially in enterprise settings or in any domain in which knowledge is constantly refreshed and cannot be memorized in the LLM. We argue here that the retrieval component of RAG systems, be it dense or sparse, deserves increased attention from the research community, and accordingly, we conduct the first comprehensive and systematic examination of the retrieval strategy of RAG systems. We focus, in particular, on the type of passages IR systems within a RAG solution should retrieve. Our analysis considers multiple factors, such as the relevance of the passages included in the prompt context, their position, and their number. One counter-intuitive finding of this work is that the retriever's highest-scoring documents that are not directly relevant to the query (e.g., do not contain the answer) negatively impact the effectiveness of the LLM. Even more surprising, we discovered that adding random documents in the prompt improves the LLM accuracy by up to 35%. These results highlight the need to investigate the appropriate strategies when integrating retrieval with LLMs, thereby laying the groundwork for future research in this area.

In recent years, Retrieval Augmented Generation (RAG) systems have emerged as a pivotal component in the field of artificial intelligence, gaining significant attention and importance across various domains. These systems, which combine the strengths of information retrieval and generative models, have shown promise in enhancing the capabilities and performance of machine learning applications. However, despite their growing prominence, RAG systems are not without their limitations and continue to be in need of exploration and improvement. This workshop seeks to focus on the critical aspect of information retrieval and its integral role within RAG frameworks. We argue that current efforts have undervalued the role of Information Retrieval (IR) in the RAG and have concentrated their attention on the generative part. As the cornerstone of these systems, IR's effectiveness dramatically influences the overall performance and outcomes of RAG models. We call for papers that will seek to revisit and emphasize the fundamental principles underpinning RAG systems. At the end of the workshop, we aim to have a clearer understanding of how robust information retrieval mechanisms can significantly enhance the capabilities of RAG systems. The workshop will serve as a platform for experts, researchers, and practitioners. We intend to foster discussions, share insights, and encourage research that underscores the vital role of Information Retrieval in the future of generative systems.

Search systems in online content platforms are typically biased toward a minority of highly consumed items, reflecting the most common user behavior of navigating toward content that is already familiar and popular. Query suggestions are a powerful tool to support query formulation and to encourage exploratory search and content discovery. However, classic approaches for query suggestions typically rely either on semantic similarity, which lacks diversity and does not reflect user searching behavior, or on a collaborative similarity measure mined from search logs, which suffers from data sparsity and is biased by highly popular queries. In this work, we argue that the task of query suggestion can be modelled as a link prediction task on a heterogeneous graph including queries and documents, enabling Graph Learning methods to effectively generate query suggestions encompassing both semantic and collaborative information. We perform an offline evaluation on an internal Spotify dataset of search logs and on two public datasets, showing that node2vec leads to an accurate and diversified set of results, especially on the large scale real-world data. We then describe the implementation in an instant search scenario and discuss a set of additional challenges tied to the specific production environment. Finally, we report the results of a large scale A/B test involving millions of users and prove that node2vec query suggestions lead to an increase in online metrics such as coverage (+1.42% shown search results pages with suggestions) and engagement (+1.21% clicks), with a specifically notable boost in the number of clicks on exploratory search queries (+9.37%).

Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r.t the best baseline model.

Deep learning-based recommender systems have become an integral part of several online platforms. However, their black-box nature emphasizes the need for explainable artificial intelligence (XAI) approaches to provide human-understandable reasons why a specific item gets recommended to a given user. One such method is counterfactual explanation (CF). While CFs can be highly beneficial for users and system designers, malicious actors may also exploit these explanations to undermine the system's security.

The rise in loosely-structured data available through text, images, and other modalities has called for new ways of querying them. Multimedia Information Retrieval has filled this gap and has witnessed exciting progress in recent years. Tasks such as search and retrieval of extensive multimedia archives have undergone massive performance improvements, driven to a large extent by recent developments in multimodal deep learning. However, methods in this field remain limited in the kinds of queries they support and, in particular, their inability to answer database-like queries. For this reason, inspired by recent work on neural databases, we propose a new framework, which we name Multimodal Neural Databases (MMNDBs). MMNDBs can answer complex database-like queries that involve reasoning over different input modalities, such as text and images, at scale. In this paper, we present the first architecture able to fulfill this set of requirements and test it with several baselines, showing the limitations of currently available models. The results show the potential of these new techniques to process unstructured data coming from different modalities, paving the way for future research in the area.

Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task and then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. Extensive experiments conducted on several tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both classification and regression tasks. Finally, to demonstrate the usefulness of our method in a real-world use case, we leverage CFs generated by ReLAX to suggest actions that a country should take to reduce the risk of mortality due to COVID-19. Interestingly enough, the actions recommended by our method correspond to the strategies that many countries have actually implemented to counter the COVID-19 pandemic.

Many recent Natural Language Processing (NLP) task formulations, such as question answering and fact verification, are implemented as a two-stage cascading architecture. In the first stage an IR system retrieves "relevant'' documents containing the knowledge, and in the second stage an NLP system performs reasoning to solve the task. Optimizing the IR system for retrieving relevant documents ensures that the NLP system has sufficient information to operate over. These recent NLP task formulations raise interesting and exciting challenges for IR, where the end-user of an IR system is not a human with an information need, but another system exploiting the documents retrieved by the IR system to perform reasoning and address the user information need. Among these challenges, as we will show, is that noise from the IR system, such as retrieving spurious or irrelevant documents, can negatively impact the accuracy of the downstream reasoning module. Hence, there is the need to balance maximizing relevance while minimizing noise in the IR system. This paper presents experimental results on two NLP tasks implemented as a two-stage cascading architecture. We show how spurious or irrelevant retrieved results from the first stage can induce errors in the second stage. We use these results to ground our discussion of the research challenges that the IR community should address in the context of these knowledge-intensive NLP tasks.

In recent years, neural networks have shown impressive performance gains on long-standing AI problems, such as answering queries from text and machine translation. These advances raise the question of whether neural nets can be used at the core of query processing to derive answers from facts, even when the facts are expressed in natural language. If so, it is conceivable that we could relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. Furthermore, such technology would enable combining information from text, images, and structured data seamlessly. This paper introduces neural databases , a class of systems that use NLP transformers as localized answer derivation engines. We ground the vision in NeuralDB, a system for querying facts represented as short natural language sentences. We demonstrate that recent natural language processing models, specifically transformers, can answer select-project-join queries if they are given a set of relevant facts. However, they cannot scale to non-trivial databases nor answer set-based and aggregation queries. Based on these insights, we identify specific research challenges that are needed to build neural databases. Some of the challenges require drawing upon the rich literature in data management, and others pose new research opportunities to the NLP community. Finally, we show that with preliminary solutions, NeuralDB can already answer queries over thousands of sentences with very high accuracy.

Machine-learned models are often perceived as “black boxes”: they are given inputs and hopefully produce desired outputs. There are many circumstances, however, where human-interpretability is crucial to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the feedback loop originated from the internals of any ensemble of decision trees to offer recommendations for transforming a k-labelled predicted instance into a k'-labelled one (for any possible pair of class labels k, k'). Our proposed algorithm perturbs individual feature values of an instance, so as to change the original prediction output by the ensemble on the so-transformed instance. This is also achieved under two constraints: the cost- and tolerance of transformation. Finally, we evaluate our approach on four distinct application domains: online advertising, healthcare, spam filtering, and handwritten digit recognition. Experiments confirm that our solution is able to suggest changes to feature values that help interpreting the rationale of model predictions, making it indeed useful in practice especially if implemented efficiently.

Over the past three years, platforms, governments and a plethora of nonprofit initiatives have prioritized fighting online misinformation through a variety of different means. Yet the current framework is too fragmented to deliver global results. The big tech platforms have data, but no public accountability. Governments (mostly) have democratic legitimacy, but little information on what is actually going on in the platforms they're itching to regulate. And nonprofit initiatives too often lack the scale to affect change at the level needed. What if we came up with a dramatically new deliberative process that involves a global community of concerned citizens ready to share information and participate in consultations to improve collective decision-making? What if a more accountable, diverse and verifiable Web were still possible?

Learning-to-Rank (LtR) solutions are commonly used in large-scale information retrieval systems such as Web search engines, which have to return highly relevant documents in response to user query within fractions of seconds. The most effective LtR algorithms adopt a gradient boosting approach to build additive ensembles of weighted regression trees. Since the required ranking effectiveness is achieved with very large ensembles, the impact on response time and query throughput of these solutions is not negligible. In this article, we propose X-CLE a VER, an iterative meta-algorithm able to build more efficient and effective ranking ensembles. X-CLE a VER interleaves the iterations of a given gradient boosting learning algorithm with pruning and re-weighting phases. First, redundant trees are removed from the given ensemble, then the weights of the remaining trees are fine-tuned by optimizing the desired ranking quality metric. We propose and analyze several pruning strategies and we assess their benefits showing that interleaving pruning and re-weighting phases during learning is more effective than applying a single post-learning optimization step. Experiments conducted using two publicly available LtR datasets show that X-CLE a VER can be successfully exploited on top of several LtR algorithms as it is effective in optimizing the effectiveness of the learnt ensembles, thus obtaining more compact forests that hence are much more efficient at scoring time.

Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model.

Sponsored search aims at retrieving the advertisements that in the one hand meet users' intent reflected in their search queries, and in the other hand attract user clicks to generate revenue. Advertisements are typically ranked based on their expected revenue that is computed as the product between their predicted probability of being clicked (i.e., namely clickability) and their advertiser provided bid. The relevance of an advertisement to a user query is implicitly captured by the predicted clickability of the advertisement, assuming that relevant advertisements are more likely to attract user clicks. However, this approach easily biases the ranking toward advertisements having rich click history. This may incorrectly lead to showing irrelevant advertisements whose clickability is not accurately predicted due to lack of click history. Another side effect consists of never giving a chance to new advertisements that may be highly relevant to be printed due to their lack of click history. To address this problem, we explicitly measure the relevance between an advertisement and a query without relying on the advertisement's click history, and present different ways of leveraging this relevance to improve user search experience without reducing search engine revenue. Specifically, we propose a machine learning approach that solely relies on text-based features to measure the relevance between an advertisement and a query. We discuss how the introduced relevance can be used in four important use cases: pre-filtering of irrelevant advertisements, recovering advertisements with little history, improving clickability prediction, and re-ranking of the advertisements on the final search result page. Offine experiments using large-scale query logs and online A/B tests demonstrate the superiority of the proposed click-oblivious relevance model and the important roles that relevance plays in sponsored search.

With the rapid proliferation of microblogging services such as Twitter, a large number of tweets is published everyday often making users feel overwhelmed with information. Helping these users to discover potentially interesting tweets is an important task for such services. In this paper, we present a novel tweet-recommendation approach, which exploits network, content, and retweet analyses for making recommendations of tweets. The idea is to recommend tweets that are not visible to the user (i.e., they do not appear in the user timeline) because nobody in her social circles published or retweeted them. To do that, we create the user's ego-network up to depth two and apply the transitivity property of the friends-of-friends relationship to determine interesting recommendations, which are then ranked to best match the user's interests. Experimental results demonstrate that our approach improves the state-of-the-art technique.

Sponsored search represents a major source of revenue for web search engines. The advertising model brings a unique possibility for advertisers to target direct user intent communicated through a search query, usually done by displaying their ads alongside organic search results for queries deemed relevant to their products or services. However, due to a large number of unique queries, it is particularly challenging for advertisers to identify all relevant queries. For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on. We present a novel advance match approach based on the idea of semantic embeddings of queries and ads. The embeddings were learned using a large data set of user search sessions, consisting of search queries, clicked ads and search links, while utilizing contextual information such as dwell time and skipped ads. To address the large-scale nature of our problem, both in terms of data and vocabulary size, we propose a novel distributed algorithm for training of the embeddings. Finally, we present an approach for overcoming a cold-start problem associated with new ads and queries. We report results of editorial evaluation and online tests on actual search traffic. The results show that our approach significantly outperforms baselines in terms of relevance, coverage and incremental revenue. Lastly, as part of this study, we open sourced query embeddings that can be used to advance the field.

Learning to Rank (LtR) is the machine learning method of choice for producing high quality document ranking functions from a ground-truth of training examples. In practice, efficiency and effectiveness are intertwined concepts and trading off effectiveness for meeting efficiency constraints typically existing in large-scale systems is one of the most urgent issues. In this paper we propose a new framework, named CLEaVER, for optimizing machine-learned ranking models based on ensembles of regression trees. The goal is to improve efficiency at document scoring time without affecting quality. Since the cost of an ensemble is linear in its size, CLEaVER first removes a subset of the trees in the ensemble, and then fine-tunes the weights of the remaining trees according to any given quality measure. Experiments conducted on two publicly available LtR datasets show that CLEaVER is able to prune up to 80% of the trees and provides an efficiency speed-up up to 2.6x without affecting the effectiveness of the model.

In this paper we focus on estimating the post-click engagement on native ads by predicting the dwell time on the corresponding ad landing pages. To infer relationships between features of the ads and dwell time we resort to the application of survival analysis techniques, which allow us to estimate the distribution of the length of time that the user will spend on the ad. This information is then integrated into the ad ranking function with the goal of promoting the rank of ads that are likely to be clicked and consumed by users (dwell time greater than a given threshold). The online evaluation over live traffic shows that considering post-click engagement has a consistent positive effect on both CTR, decreases the number of bounces and increases the average dwell time, hence leading to a better user post-click experience.

Click-through rate (CTR) is the most common metric used to assess the performance of an online advert; another performance of an online advert is the user post-click experience. In this paper, we describe the method we have implemented in Yahoo Gemini to measure the post-click experience on Yahoo mobile news streams via an automatic analysis of advert landing pages. We measure the post-click experience by means of two well-known metrics, dwell time and bounce rate. We show that these metrics can be used as proxy of an advert post-click experience, and that a negative post-click experience has a negative effect on user engagement and future ad clicks. We then put forward an approach that analyses advert landing pages, and show how these can affect dwell time and bounce rate. Finally, we develop a prediction model for advert quality based on dwell time, which was deployed on Yahoo mobile news stream app running on iOS. The results show that, using dwell time as a proxy of post-click experience, we can prioritise higher quality ads. We demonstrate the impact of this on users via A/B testing.

Search engines represent one of the most popular web services, visited by more than 85% of internet users on a daily basis. Advertisers are interested in making use of this vast business potential, as very clear intent signal communicated through the issued query allows effective targeting of users. This idea is embodied in a sponsored search model, where each advertiser maintains a list of keywords they deem indicative of increased user response rate with regards to their business. According to this targeting model, when a query is issued all advertisers with a matching keyword are entered into an auction according to the amount they bid for the query, and the winner gets to show their ad. One of the main challenges is the fact that a query may not match many keywords, resulting in lower auction value, lower ad quality, and lost revenue for advertisers and publishers. Possible solution is to expand a query into a set of related queries and use them to increase the number of matched ads, called query rewriting. To this end, we propose rewriting method based on a novel query embedding algorithm, which jointly models query content as well as its context within a search session. As a result, queries with similar content and context are mapped into vectors close in the embedding space, which allows expansion of a query via simple K-nearest neighbor search in the projected space. The method was trained on more than 12 billion sessions, one of the largest corpuses reported thus far, and evaluated on both public TREC data set and in-house sponsored search data set. The results show the proposed approach significantly outperformed existing state-of-the-art, strongly indicating its benefits and the monetization potential.

Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.

In this article, we tackle the problem of predicting the “next” geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist’s current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in recommender and trail prediction systems for tourism, as well as a popularity baseline. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions.

In this paper, we tackle the problem of predicting the "next" geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.

A search engine infrastructure must be able to provide the same quality of service to all queries received during a day. During normal operating conditions, the demand for resources is considerably lower than under peak conditions, yet an oversized infrastructure would result in an unnecessary waste of computing power. A possible solution adopted in this situation might consist of defining a maximum threshold processing time for each query, and dropping queries for which this threshold elapses, leading to disappointed users. In this paper, we propose and evaluate a different approach, where, given a set of different query processing strategies with differing efficiency, each query is considered by a framework that sets a maximum query processing time and selects which processing strategy is the best for that query, such that the processing time for all queries is kept below the threshold. The processing time estimates used by the scheduler are learned from past queries. We experimentally validate our approach on 10,000 queries from a standard TREC dataset with over 50 million documents, and we compare it with several baselines. These experiments encompass testing the system under different query loads and different maximum tolerated query response times. Our results show that, at the cost of a marginal loss in terms of response quality, our search system is able to answer 90% of queries within half a second during times of high query volume.

Although Web search engines still answer user queries with lists of ten blue links to webpages, people are increasingly issuing queries to accomplish their daily tasks (e.g., finding a recipe , booking a flight , reading online news , etc.). In this work, we propose a two-step methodology for discovering tasks that users try to perform through search engines. First, we identify user tasks from individual user sessions stored in search engine query logs. In our vision, a user task is a set of possibly noncontiguous queries (within a user search session), which refer to the same need. Second, we discover collective tasks by aggregating similar user tasks, possibly performed by distinct users. To discover user tasks, we propose query similarity functions based on unsupervised and supervised learning approaches. We present a set of query clustering methods that exploit these functions in order to detect user tasks. All the proposed solutions were evaluated on a manually-built ground truth, and two of them performed better than state-of-the-art approaches. To detect collective tasks, we propose four methods that cluster previously discovered user tasks, which in turn are represented by the bag-of-words extracted from their composing queries. These solutions were also evaluated on another manually-built ground truth.

Recommender systems have become ubiquitous in content-based web applications, from news to shopping sites. Nonetheless, an aspect that has been largely overlooked so far in the recommender system literature is that of automatically building explanations for a particular recommendation. This paper focuses on the news domain, and proposes to enhance effectiveness of news recommender systems by adding, to each recommendation, an explanatory statement to help the user to better understand if, and why, the item can be her interest. We consider the news recommender system as a black-box, and generate different types of explanations employing pieces of information associated with the news. In particular, we engineer text-based, entity-based, and usage-based explanations, and make use of a Markov Logic Networks to rank the explanations on the basis of their effectiveness. The assessment of the model is conducted via a user study on a dataset of news read consecutively by actual users. Experiments show that news recommender systems can greatly benefit from our explanation module as it allows users to discriminate between interesting and not interesting news in the majority of the cases.

Collaborative content creation and annotation creates vast repositories of all sorts of media, and user-defined tags play a central role as they are a simple yet powerful tool for organizing, searching and exploring the available resources. We observe that when a user annotates a resource with a set of tags, those tags are introduced one at a time. Therefore, when the fourth tag is introduced, a knowledge represented by the previous three tags, i.e., the context in which the fourth tag is produced, is available and exploitable for generating potential correction of the current tag. This context, together with the "wisdom of the crowd" represented by the co-occurrences of tags in all the resources of the repository, can be exploited to provide interactive tag spell check and correction. We develop this idea in a framework, based on a weighted tag co-occurrence graph and on nodes relatedness measures defined on weighted neighborhoods. We test our proposal on a dataset coming from YouTube. The results show that our framework is effective as it outperforms two important baselines. We also show that it is efficient, thus enabling its use in modern tagging services.

In this paper we introduce the task of "tweet recommendation", the problem of suggesting tweets that match a user's interests and likes. We propose an Information-Retrieval-like model that leverages the content of the user's tweets and those of her friends, and that effectively retrieves a set of tweets that is personalized and varied in nature. Our approach could be easily leveraged to build, for example, a Twitter or Facebook timeline that collects messages that are of interest for the user, but that are not posted by her friends. We compare to typical approaches used in similar tasks, reporting significant gains in terms of overall precision, up to about +20%, on both a corpus-based evaluation and real world user study.

We present a recommendation method based on the well-known concept of center-piece subgraph, that allows for the time/space efficient generation of suggestions also for rare, i.e., long-tail queries. Our method is scalable with respect to both the size of datasets from which the model is computed and the heavy workloads that current web search engines have to deal with. Basically, we relate terms contained into queries with highly correlated queries in a query-flow graph. This enables a novel recommendation generation method able to produce recommendations for approximately 99% of the workload of a real-world search engine. The method is based on a graph having term nodes, query nodes, and two kinds of connections: term-query and query-query. The first connects a term to the queries in which it is contained, the second connects two query nodes if the likelihood that a user submits the second query after having issued the first one is sufficiently high. On such large graph we need to compute the center-piece subgraph induced by terms contained into queries. In order to reduce the cost of the above computation, we introduce a novel and efficient method based on an inverted index representation of the model. We experiment our solution on two real-world query logs and we show that its effectiveness is comparable (and in some case better) than state-of-the-art methods for head-queries. More importantly, the quality of the recommendations generated remains very high also for long-tail queries, where other methods fail even to produce any suggestion. Finally, we extensively investigate scalability and efficiency issues and we show the viability of our method in real world search engines.

We investigate the impact of query result prefetching on the efficiency and effectiveness of web search engines. We propose offline and online strategies for selecting and ordering queries whose results are to be prefetched. The offline strategies rely on query log analysis and the queries are selected from the queries issued on the previous day. The online strategies select the queries from the result cache, relying on a machine learning model that estimates the arrival times of queries. We carefully evaluate the proposed prefetching techniques via simulation on a query log obtained from Yahoo! web search. We demonstrate that our strategies are able to improve various performance metrics, including the hit rate, query response time, result freshness, and query degradation rate, relative to a state-of-the-art baseline.

Web Search Engines ’ result pages contain references to the top-k documents relevant for the query submitted by a user. Each document is represented by a title, a snippet and a URL. Snippets, i.e. short sentences showing the portions of the document being relevant to the query, help users to select the most interesting results. The snippet generation process is very expensive, since it may require to access a number of documents for each issued query. We assert that caching, a popular technique used to enhance performance at various levels of any computing sys-tems, can be very effective in this context. We design and experiment several cache organizations, and we introduce the concept of supersnippet, that is the set of sentences in a document that are more likely to answer future queries. We show that supersnippets can be built by exploiting query logs, and that in our experiments a supersnippet cache an-swers up to 62 % of the requests, remarkably outperforming other caching approaches.

The research challenge addressed in this paper is to devise effective techniques for identifying task-based sessions, i.e. sets of possibly non contiguous queries issued by the user of a Web Search Engine for carrying out a given task. In order to evaluate and compare different approaches, we built, by means of a manual labeling process, a ground-truth where the queries of a given query log have been grouped in tasks. Our analysis of this ground-truth shows that users tend to perform more than one task at the same time, since about 75% of the submitted queries involve a multi-tasking activity. We formally define the Task-based Session Discovery Problem (TSDP) as the problem of best approximating the manually annotated tasks, and we propose several variants of well known clustering algorithms, as well as a novel efficient heuristic algorithm, specifically tuned for solving the TSDP. These algorithms also exploit the collaborative knowledge collected by Wiktionary and Wikipedia for detecting query pairs that are not similar from a lexical content point of view, but actually semantically related. The proposed algorithms have been evaluated on the above ground-truth, and are shown to perform better than state-of-the-art approaches, because they effectively take into account the multi-tasking behavior of users.

In this paper we analyze the efficiency of various search results diversification methods. While efficacy of diversification approaches has been deeply investigated in the past, response time and scalability issues have been rarely addressed. A unified framework for studying performance and feasibility of result diversification solutions is thus proposed. First we define a new methodology for detecting when, and how, query results need to be diversified. To this purpose, we rely on the concept of "query refinement" to estimate the probability of a query to be ambiguous . Then, relying on this novel ambiguity detection method, we deploy and compare on a standard test set, three different diversification methods: IASelect, xQuAD, and OptSelect. While the first two are recent state-of-the-art proposals, the latter is an original algorithm introduced in this paper. We evaluate both the efficiency and the effectiveness of our approach against its competitors by using the standard TREC Web diversification track testbed. Results shown that OptSelect is able to run two orders of magnitude faster than the two other state-of-the-art approaches and to obtain comparable figures in diversification effectiveness.

Given a set of records, a threshold value t and a similarity function, we investigate the problem of finding all pairs of records such that similarity between each pair is above t. We propose several optimizations on the existing approaches to solve the problem. Our algorithm outperforms the state-of-the-art algorithms in the case with large and high-dimensional datasets. The speedup we achieved varied from 30% to 4-x depending on the similarity threshold and the dataset properties.

Encoding lists of integers efficiently is important for many applications in different fields. Adjacency lists of large graphs are usually encoded to save space and to improve decoding speed. Inverted indexes of Information Retrieval systems keep the lists of postings compressed in order to exploit the memory hierarchy. Secondary indexes of DBMSs are stored similarly to inverted indexes in IR systems. In this paper we propose Vector of Splits Encoding (VSEncoding), a novel class of encoders that work by optimally partitioning a list of integers into blocks which are efficiently compressed by using simple encoders. In previous works heuristics were applied during the partitioning step. Instead, we find the optimal solution by using a dynamic programming approach. Experiments show that our class of encoders outperform all the existing methods in literature by more than 10% (with the exception of Binary Interpolative Coding with which they, roughly, tie) still retaining a very fast decompression algorithm.

Static index pruning techniques aim at removing from the posting lists of an inverted file the references to documents which are likely to be not relevant for answering user queries. The reduction in the size of the index results in a better exploitation of memory hierarchies and faster query processing. On the other hand, pruning may affect the precision of the information retrieval system, since pruned entries are unavailable at query processing time. Static pruning techniques proposed so far exploit query-independent measures to evaluate the importance of a document within a posting list. This paper proposes a general framework that aims at enhancing the precision of any static pruning methods by exploiting usage information extracted from query logs. Experiments conducted on the TREC WT10g Web collection and a large Altavista query log show that integrating usage knowledge into the pruning process is profitable, and increases remarkably performance figures obtained with the state-of-the art Carmel's static pruning method.

This article introduces an architecture for a document-partitioned search engine, based on a novel approach combining collection selection and load balancing, called load-driven routing . By exploiting the query-vector document model, and the incremental caching technique, our architecture can compute very high quality results for any query, with only a fraction of the computational load used in a typical document-partitioned architecture. By trading off a small fraction of the results, our technique allows us to strongly reduce the computing pressure to a search engine back-end; we are able to retrieve more than 2/3 of the top-5 results for a given query with only 10% the computing load needed by a configuration where the query is processed by each index partition. Alternatively, we can slightly increase the load up to 25% to improve precision and get more than 80% of the top-5 results. In fact, the flexibility of our system allows a wide range of different configurations, so as to easily respond to different needs in result quality or restrictions in computing power. More important, the system configuration can be adjusted dynamically in order to fit unexpected query peaks or unpredictable failures. This article wraps up some recent works by the authors, showing the results obtained by tests conducted on 6 million documents, 2,800,000 queries and real query cost timing as measured on an actual index.

World Wide Web content continuously grows in size and importance. Furthermore, users ask Web search engines to satisfy increasingly disparate information needs. New techniques and tools are constantly developed aimed at assisting users in the interaction with the Web search engine. Query recommender systems suggesting interesting queries to users are an example of such tools. Most query recommendation techniques are based on the knowledge of the behaviors of past users of the search engine recorded in query logs.

The recommendation of queries, known as query suggestion, is a common practice on major Web Search Engines. It aims to help users to find the information they are looking for, and is usually based on the knowledge learned from past interactions with the search engine. In this paper we propose a new model for query suggestion, the Search Shortcut Problem, that consists in recommending "successful" queries that allowed other users to satisfy, in the past, similar information needs. This new model has several advantages with respect to traditional query suggestion approaches. First, it allows a straightforward evaluation of algorithms from available query log data. Moreover, it simplifies the application of several recommendation techniques from other domains. Particularly, in this work we applied Collaborative Filtering to this problem, and evaluated the interesting results achieved on large query logs from AOL and Microsoft. Different techniques for analyzing and extracting information from query logs, as well as new metrics and techniques for measuring the effectiveness of recommendations are proposed and evaluated. The results obtained clearly show the importance of several of our contributions, and open an interesting field for future research.

Giving suggestions to users of Web-based services is a common practice aimed at enhancing their navigation experience. Major Web Search Engines usually provide "Suggestions" under the form of queries that are, to some extent, related to the current query typed by the user, and the knowledge learned from the past usage of the system. In this work we introduce "Search Shortcuts" as "Successful" queries allowed, in the past, users to satisfy their information needs. Differently from conventional suggestion techniques, our search shortcuts allows to evaluate effectiveness by exploiting a simple train-and-test approach. We have applied several Collaborative Filtering algorithms to this problem, evaluating them on a real query log data. We generate the shortcuts from all user sessions belonging to the testing set, and measure the quality of the shortcuts suggested by considering the similarity between them and the navigational user behavior.

In this article we study the trade-offs in designing efficient caching systems for Web search engines. We explore the impact of different approaches, such as static vs. dynamic caching, and caching query results vs. caching posting lists. Using a query log spanning a whole year, we explore the limitations of caching and we demonstrate that caching posting lists can achieve higher hit rates than caching query answers. We propose a new algorithm for static caching of posting lists, which outperforms previous methods. We also study the problem of finding the optimal way to split the static cache between answers and posting lists. Finally, we measure how the changes in the query log influence the effectiveness of static caching, given our observation that the distribution of the queries changes slowly over time. Our results and observations are applicable to different levels of the data-access hierarchy, for instance, for a memory/disk layer or a broker/remote server layer.

In the ocean of Web data, Web search engines are the primary way to access content. As the data is on the order of petabytes, current search engines are very large centralized systems based on replicated clusters. Web data, however, is always evolving. The number of Web sites continues to grow rapidly and there are currently more than 20 billion indexed pages. In the near future, centralized systems are likely to become ineffective against such a load, thus suggesting the need of fully distributed search engines. Such engines need to achieve the following goals: high quality answers, fast response time, high query throughput, and scalability. In this paper we survey and organize recent research results, outlining the main challenges of designing a distributed Web retrieval system.

In this paper we study the trade-offs in designing efficient caching systems for Web search engines. We explore the impact of different approaches, such as static vs. dynamic caching, and caching query results vs.caching posting lists. Using a query log spanning a whole year we explore the limitations of caching and we demonstrate that caching posting lists can achieve higher hit rates than caching query answers. We propose a new algorithm for static caching of posting lists, which outperforms previous methods. We also study the problem of finding the optimal way to split the static cache between answers and posting lists. Finally, we measure how the changes in the query log affect the effectiveness of static caching, given our observation that the distribution of the queries changes slowly over time. Our results and observations are applicable to different levels of the data-access hierarchy, for instance, for a memory/disk layer or a broker/remote server layer.

Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that combines link-based and content-based features, and uses the topology of the Web graph by exploiting the link dependencies among the Web pages. We find that linked hosts tend to belong to the same class: either both are spam or both are non-spam. We demonstrate three methods of incorporating the Web graph topology into the predictions obtained by our base classifier: (i) clustering the host graph, and assigning the label of all hosts in the cluster by majority vote, (ii) propagating the predicted labels to neighboring hosts, and (iii) using the predicted labels of neighboring hosts as new features and retraining the classifier. The result is an accurate system for detecting Web spam, tested on a large and public dataset, using algorithms that can be applied in practice to large-scale Web data.

No abstract available.

This article discusses efficiency and effectiveness issues in caching the results of queries submitted to a Web search engine (WSE). We propose SDC (Static Dynamic Cache), a new caching strategy aimed to efficiently exploit the temporal and spatial locality present in the stream of processed queries. SDC extracts from historical usage data the results of the most frequently submitted queries and stores them in astatic,read-onlyportion of the cache. The remaining entries of the cache are dynamically managed according to a given replacement policy and are used for those queries that cannot be satisfied by the static portion. Moreover, we improve the hit ratio of SDC by using an adaptive prefetching strategy, which anticipates future requests by introducing a limited overhead over the back-end WSE. We experimentally demonstrate the superiority of SDC over purely static and dynamic policies by measuring the hit ratio achieved on three large query logs by varying the cache parameters and the replacement policy used for managing the dynamic part of the cache. Finally, we deploy and measure the throughput achieved by a concurrent version of our caching system. Our tests show how the SDC cache can be efficiently exploited by many threads that concurrently serve the queries of different users.

In this demo we show a recommender system, called SUGGEST , that dynamically generates links to pages that have not yet been visited by a user and might be of his potential interest. Usually other recommender systems exploit a kind of two-phase architecture composed by an off-line component that analyzes Web server access logs and generates information used by a successive online component that generates recommendations. SUGGEST collapse the two-phase into a single online Apache module. The component is able to manage very large Web sites made up of dinamically generated pages by means of an efficient LRU-based database management strategy. The demo will show the way SUGGEST is able to anticipate users’ requests that will be made farther in the future, introducing a limited overhead on the Web server activity.

Web Search Engines provide a large-scale text document retrieval service by processing huge Inverted File indexes. Inverted File indexes allow fast query resolution and good memory utilization since their d-gaps representation can be effectively and efficiently compressed by using variable length encoding methods. This paper proposes and evaluates some algorithms aimed to find an assignment of the document identifiers which minimizes the average values of d-gaps, thus enhancing the effectiveness of traditional compression methods. We ran several tests over the Google contest collection in order to validate the techniques proposed. The experiments demonstrated the scalability and effectiveness of our algorithms. Using the proposed algorithms, we were able to sensibly improve (up to 20.81%) the compression ratios of several encoding schemes.

In this paper we propose a WUM recommender system, called SUGGEST 3.0, that dynamically generates links to pages that have not yet been visited by a user and might be of his potential interest. Differently from the recommender systems proposed so far, SUGGEST 3.0 does not make use of any off-line component, and is able to manage Web sites made up of pages dynamically generated. To this purpose SUGGEST 3.0 incrementally builds and maintains historical information by means of an incremental graph partitioning algorithm, requiring no off-line component. The main innovation proposed here is a novel strategy that can be used to manage large Web sites. Experiments, conducted in order to evaluate SUGGEST 3.0 performance, demonstrated that our system is able to anticipate users' requests that will be made farther in the future, introducing a limited overhead on the Web server activity.

Today, the development of Grid applications is considered a nightmare, due to lack of grid programming environments, standards, off-the-shelf software components, and so on.

The performance of an algorithm that mines frequent sets from transactional databases may severely depend on the specific features of the data being analyzed. Moreover, some architectural characteristics of the computational platform used - e.g. the available main memory - can dramatically change its runtime behavior. In this paper we present DCI (Direct Count & Intersect), an efficient algorithm for discovering frequent sets from large databases. Due to the multiple heuristics strategies adopted, DCI can adapt its behavior not only to the features of the specific computing platform, but also to the features of the dataset being mined, so that it results very effective in mining both short and long patterns from sparse and dense datasets. Finally we also discuss the parallelization strategies adopted in the design of ParDCI, a distributed and multi-threaded implementation of DCI.