Autonomous decision-making systems, particularly recommender systems, have received increasing attention concerning fairness, i.e., if all stakeholders affected by such a system are treated equally as a result of the recommendations. Existing approaches primarily focus on fairness between two stakeholders – consumers and providers or consumers and items – treating providers and items as the same entity. However, we argue for the treatment of providers and items as distinct stakeholders to offer more comprehensive models of fairness in recommender systems. To this end, we propose a fairness-aware recommender system, CIPFRS, designed to optimize fairness across all three key stakeholders: consumers, providers, and items. We examine consumer fairness regarding their level of interaction with the system; high and low-activity users should be treated equally. Further, all providers should have an equal opportunity for their products to be recommended. Finally, we propose an approach to implement item fairness in each provider’s inventory. We report an extensive evaluation of the proposed solution through three datasets, demonstrating that considering all three stakeholders yields improved recommendations while minimizing bias.

Sequential task assignment is a crucial process in many contexts, where resource allocation over time is a key step to consider and often involves groups of people with diverse objectives, preferences, and constraints. Fairness in these scenarios is paramount, as it implies efficiency and satisfaction while also impacting performance. Although the definition of fairness depends on the context and domain, it generally ensures an equal distribution of tasks among participants, subject to certain constraints and guidelines. Moreover, it mitigates biases and disparities, promoting inclusivity and diversity within teams. In this paper, we highlight the different aspects of fairness in sequential task assignments and emphasize that the perspectives of various stakeholders must be considered. As motivating examples, we concentrate on two scenarios: (a) the timetable creation problem in the university domain, showing that the notion of fairness must be considered from both the students’ and professors’ points of view, and (b) the tourism traveling planning, where the perspectives of tour guides and tourists are taken into account during a planning process. We propose a generic formalization of the problem that an optimization algorithm can easily manage. The aim is to find and compare the fairness of different stakeholders and evaluate whether a fair solution for one of them can be fair for another with different constraints and preferences. We introduce the notion of local and global fairness to highlight that an optimal solution for one stakeholder does not necessarily mean it is optimal also for others, and some compromises need to be identified. Finally, we explore how global fairness can be achieved by integrating multiple solutions, each aligned with a local fairness perspective.

Entity Resolution (ER) is a fundamental task in data integration, particularly in streaming environments where entities arrive continuously and decisions must be made under strict time constraints. Existing approaches primarily optimize efficiency and accuracy, but often overlook the interpretability of matching decisions and may propagate group-level disparities. To address these limitations, this paper introduces X-TREATS, a streaming-oriented ER workflow that integrates pair-level explanations directly into the resolution pipeline, jointly combining similarity, fairness, and explanation signals during ranking step. The proposed approach is evaluated on real-world datasets under incremental processing, assessing effectiveness, fairness, and explainability. Experimental results show that X-TREATS substantially improves interpretability, increasing the Explanation Score by up to 20%, while reducing group-level disparities by 40–70% and preserving high matching precision across all datasets. These findings demonstrate the practical benefits of integrating explanation-aware mechanisms into real-time ER pipelines.

Recommender systems often aim to serve multiple stakeholders, such as consumers and providers, each with distinct fairness expectations. While fairness-aware recommendation has gained increasing attention, most existing methods assume unlimited item availability. However, real-world scenarios often involve limited supply, where only a small number of item copies can be allocated. This creates new challenges in balancing fair exposure, equitable access, and relevance. In this paper, we propose a multisided fairness-aware recommendation framework designed for settings with limited item availability. Our approach explicitly models fairness both across stakeholders, ensuring consumers and providers are treated equitably compared to their peers, and within consumer–provider relationships, ensuring stakeholders treat their counterparts fairly. We formalize these as inter- and intra-stakeholder fairness and introduce evaluation metrics that measure treatment consistency under supply constraints. To address the allocation challenge, we develop a novel algorithm that assigns limited items while jointly optimizing for fairness and relevance. We evaluate our method on real-world datasets from Amazon and Goodreads, showing that it mitigates bias toward highly active users and dominant providers. Compared to conventional recommendation algorithms, our approach reduces fairness disparities by up to 80 %, underscoring the importance of fairness-aware design in real-world, resource-constrained recommendation scenarios.

Currently, the growing proliferation of information systems generates large volumes of data continuously, stemming from a variety of sources such as web platforms, social networks, and multiple devices. These data, often lacking a defined schema, require an initial process of consolidation and cleansing before analysis and knowledge extraction can occur. In this context, Entity Resolution (ER) plays a crucial role, facilitating the integration of knowledge bases and identifying similarities among entities from different sources. However, the traditional ER process is computationally expensive, and becomes more complicated in the streaming context where the data arrive continuously. Moreover, there is a lack of studies involving fairness and ER, which is related to the absence of discrimination or bias. In this sense, fairness criteria aim to mitigate the implications of data bias in ER systems, which requires more than just optimizing accuracy, as traditionally done. Considering this context, this work presents TREATS, a schema-agnostic and fairness-aware ER workflow developed for managing streaming data incrementally. The proposed fairness-aware ER framework tackles constraints across various groups of interest, presenting a resilient and equitable solution to the related challenges. Through experimental evaluation, the proposed techniques and heuristics are compared against state-of-the-art approaches over five real-world data source pairs, in which the results demonstrated significant improvements in terms of fairness, without degradation of effectiveness and efficiency measures in the streaming environment. In summary, our contributions aim to propel the ER field forward by providing a workflow that addresses both technical challenges and ethical concerns.

In group recommendation systems, achieving a balance between fairness and diversity is a challenging yet crucial task, particularly in sequential settings where preferences evolve over multiple iterations. This paper introduces ADAPT, a novel framework designed to optimize fairness and diversity in sequential group recommendations. ADAPT employs two novel aggregation methods, FaDJO and DiGSFO, to equitably meet group members’ needs while promoting diverse content. In addition to the novel aggregation methods ADAPT introduces a novel definition for the inter-round diversity based on item-lists embeddings. Experimental results on two real datasets and different group formation demonstrate ADAPT’s ability to optimize user satisfaction, fairness, and diversity, outperforming baseline methods in two different metrics (f-score and NDCG) and highlighting the importance of balancing these critical factors in sequential group settings.

Companies and organizations monitor customer satisfaction by collecting feedback through Likert scale questions and free-text responses. Freely expressed opinions, not bound to fixed questions, provide a detailed source of information that organizations can use to improve their daily operations. The organization’s quality assurance review processes require a timely follow-up on these customer opinions. However, solutions often address the analytics of textual information with topic discovery and sentiment analysis for a fixed time period. These frameworks also tend to focus on serving the purpose of a specific domain and terminology. In this study, we focus on a facilitation service to track discovered topics and their sentiments over time. This service is generic and can be applied to different domains. To evaluate the capabilities of the framework, we used two datasets with opposite types of wording. The study shows that the framework is capable of discovering similar topics over time and identifying their sentiment changes.

Explainable Recommendation Systems (RS) enhance the user experience on online platforms by recommending personalized content, as well as explanations for the given recommendations to add transparency and build up trust in the platforms. Extending the notion of explainable RS, in this paper we define Why-Not explanations for recommendations that were expected but not returned, and propose and implement a technique for computing Why-Not explanations in a post-hoc manner for a graph-based RS. Our approach builds on the notion of counterfactual explanations in the means of a set of user-rooted edges to add or remove in the graph, in order to place the missing recommendation to the top of the recommendation list, and provides in this way actionable insights on the source data and their interrelations. Our experimental evaluation on a real-world data set demonstrates the feasibility of our proposal and reveals interesting directions for future work.

Dealing with many of the problems related to the quality of textual content online involves identifying similar content. Algorithmic solutions for duplicate content classification typically rely on text vector representation, which maps textual information into a set of features. Ideally, this representation would capture all aspects of the underlying text, including length, word frequencies, syntax, and semantics. While recent advancements in text representation have led to improved performance, a comprehensive approach that explicitly incorporates all text features has not yet been proposed. In this study, we present the INCEPT framework that utilizes multiple representation methods to detect duplicate text pairs, taking advantage of their individual strengths. The core of our approach involves using a stacking ensemble of pairwise vector distance measurements that are computed from multiple text representation methods. A stacking classifier then utilizes these distance scores as input and learns to identify duplicate posts. We assess the proposed framework’s effectiveness in identifying duplicate posts in an online Question and Answer platform. By combining several text representation methods, INCEPT performs well in the duplicate posts classification task. Our experiments demonstrate that specific framework configurations outperform the accuracy scores obtained from individual text representation methods. Therefore, we also infer that no single text representation method can independently capture a text’s features.

Nowadays, sequential recommendations are becoming more prevalent. A user expects the system to remember past interactions and not conduct each recommendation round as a stand-alone process. Additionally, group recommendation systems are more prominent since more and more people are able to form groups for activities. Subsequently, the data that a group recommendation system needs to consider becomes more complicated — historical data and feedback for each user, the items recommended and ultimately selected to and by the group, etc. This makes the selection of a group recommendation algorithm to be even more complex. In this work, we propose the SQUIRREL framework — SeQUentIal Recommendations with ReinforcEment Learning, a model that relies on reinforcement learning techniques to select the most appropriate group recommendation algorithm based on the current state of the group. At each round of recommendations, we calculate the satisfaction of each group member, how relevant each item in the group recommendation list is for each user, and based on this the model selects an action, that is, a recommendation algorithm out of a predefined set that will produce the maximum reward. We present a sample of methods that can be used; however, the model is able to be further configured with additional actions, different definitions of rewards or states. We perform experiments on three real world datasets, 20M MovieLens, GoodReads and Amazon, and show that SQUIRREL is able to outperform all the individual recommendation methods used in the action set, by correctly identifying the recommendation algorithm that maximizes the reward function utilized.

Abstract Recently, group recommendations have gained much attention. Nevertheless, most approaches consider only one round of recommendations. However, in a real-life scenario, it is expected that the history of previous recommendations is exploited to tailor the recommendations towards meeting the needs of the group members. Such history should include not only which items the system suggested, but also the reaction of the members to these items. This work introduces the problem of sequential group recommendations, by exploiting the concept of satisfaction and disagreement. Satisfaction describes how well the group received the suggested items. Disagreement describes the satisfaction bias among the group members. We utilize these concepts in three new aggregation methods, SDAA, SIAA and Average+, designed to address the specific challenges introduced by sequential group recommendations. We experimentally show the effectiveness of our methods using big real datasets for both stable and ephemeral groups.

Abstract Recommender systems were originally proposed for suggesting potentially relevant items to users, with the unique objective of providing accurate suggestions. These recommenders started being adopted in several domains, and were identified as generating biased results that could harm the data items being recommended. The exposure in generated rankings, for instance in a job candidate selection situation, is supposed to be fairly distributed among candidates, regardless of their sensitive attributes (gender, race, nationality, age) for promoting equal opportunities. It can happen, however, that no such sensitive information is available in the data applied for training the recommender, and in this case, there is still space for biases that can lead to unfair treatment, named Feature-Blind unfairness. In this work, we adopt Variational Autoencoders (VAE), considered as the state-of-the-art technique for Collaborative Filtering (CF) recommendations, and we present a framework for addressing fairness when having only access to information about user-item interactions. More specifically, we are interested in Position and Popularity Bias. VAE loss function combines two terms associated with accuracy and quality of representation; we introduce a new term for encouraging fairness, and demonstrate the effect of promoting fair results despite of a tolerable decrease in recommendation quality. In our best scenario, position bias is reduced by 42% despite a reduction of 26% in recall in the top 100 recommendation results, compared to the same situation without any fairness constraints.

Abstract We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems among others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this work, we aim at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are threefold: (a) to provide a solid framework on a novel, quickly evolving and impactful domain, (b) to present related methods and put them into perspective and (c) to highlight open challenges and research paths for future work.

There is an urgent call to detect and prevent "biased data" at the earliest possible stage of the data pipelines used to build automated decision-making systems. In this paper, we are focusing on controlling the data bias in entity resolution (ER) tasks aiming to discover and unify records/descriptions from different data sources that refer to the same real-world entity. We formally define the ER problem with fairness constraints ensuring that all groups of entities have similar chances to be resolved. Then, we introduce FairER, a greedy algorithm for solving this problem for fairness criteria based on equal matching decisions. Our experiments show that FairER achieves similar or higher accuracy against two baseline methods over 7 datasets, while guaranteeing minimal bias.

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommendation systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. This tutorial aims at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are three-fold: (a) to provide a solid framework on a novel, quickly evolving, and impactful domain, (b) to present related methods and put them into perspective, and (c) to highlight challenges and research paths for researchers and practitioners that work in data management and applications.

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommender systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. In this tutorial, we aim at presenting a toolkit of methods used for ensuring fairness in rankings and recommendations. Our objectives are two-fold: (a) to present related methods of this novel, quickly evolving and impactful domain, and put them into perspective, and (b) to highlight open challenges and research paths for future work.

Peer reviewed

To support historians in their work, we need to understand their work‐related needs and propose what is required to support those needs. Although the quantity of digitized historical documents available is increasing, historians' ways of working with the digital documents have not been widely studied, particularly in authentic work settings. To better support the historians' reasoning processes, we investigate history researchers' work tasks as the context of information interaction and examine their cognitive access points into information. The analysis is based on a longitudinal observational research and interviews in a task‐based research setting. Based on these findings in the historians' cognitive space, we build bridges into the document space. By studying the information interactions in real task contexts, we facilitate the provision of task‐specific handles into documents that can be used in designing digital research tools for historians.

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of entity descriptions published in the Web of Data. To address them, we propose the MinoanER framework that simultaneously fulfills full automation, support of highly heterogeneous entities, and massive parallelization of the ER process. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, as they are indicated only by statistics. A composite blocking method is employed to capture different sources of matching evidence from the content, neighbors, or names of entities. The search space of candidate pairs for comparison is compactly abstracted by a novel disjunctive blocking graph and processed by a non-iterative, massively parallel matching algorithm that consists of four generic, schema-agnostic matching rules that are quite robust with respect to their internal configuration. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low Variety, but it outperforms them significantly when matching KBs with high Variety.

The widespread use of information systems has become a valuable source of semi-structured data. In this context, Entity Resolution (ER) emerges as a fundamental task to integrate multiple knowledge bases or identify similarities between data items (i.e., entities). Since ER is an inherently quadratic task, blocking techniques are often used to improve efficiency. Beyond the challenges related to the data volume and heterogeneity, blocking techniques also face two other challenges: streaming data and incremental processing. To address these challenges, we propose PRIME, a novel incremental schema-agnostic blocking technique that utilizes parallelism to enhance blocking efficiency. The proposed technique deals with streaming and incremental data using a distributed computational infrastructure. To improve efficiency, the technique avoids unnecessary comparisons and applies a time window strategy to prevent excessive memory consumption.

The focus of this work is on the problem of team recommendations, in which teams have multidisciplinary requirements and team members’ selection is based on the match of their skills and the requirements. When assembling multiple teams there is also a challenge of allocating the best members in a fair way between the teams. We formally define the problem and propose a brute force and a faster heuristic method as solutions to create team recommendations to multidisciplinary projects. Furthermore, to increase the fairness between the recommended teams, the K-rounds and Pairs-rounds methods are proposed as variations of the heuristic approach. Several different test scenarios are executed to analyze and compare the effectiveness of these methods.

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of descriptions published in the Web of Data. To address them, we propose the MinoanER framework that fulfills full automation and support of highly heterogeneous entities. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, indicated only by statistics. For high efficiency, similarities are computed from a set of schema-agnostic blocks and processed in a non-iterative way that involves four threshold-free heuristics. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low heterogeneity in terms of entity types and content. Yet, MinoanER outperforms state-of-the-art ER tools when matching highly heterogeneous KBs.

Entity resolution aims to identify descriptions of the same entity within or across knowledge bases. In this work, we provide a comprehensive and cohesive overview of the key research results in the area of entity resolution. We are interested in frameworks addressing the new challenges in entity resolution posed by the Web of data in which real world entities are described by interlinked data rather than documents. Since such descriptions are usually partial, overlapping and sometimes evolving, entity resolution emerges as a central problem both to increase dataset linking, but also to search the Web of data for entities and their relations. We focus on Web-scale blocking, iterative and progressive solutions for entity resolution. Specifically, to reduce the required number of comparisons, blocking is performed to place similar descriptions into blocks and executes comparisons to identify matches only between descriptions within the same block. To minimize the number of missed matches, an iterative entity resolution process can exploit any intermediate results of blocking and matching, discovering new candidate description pairs for resolution. Finally, we overview works on progressive entity resolution, which attempt to discover as many matches as possible given limited computing budget, by estimating the matching likelihood of yet unresolved descriptions, based on the matches found so far.

As knowledge bases are constantly evolving, there is a clear need for monitoring and analyzing the changes that occur on them. Traditional approaches for studying the evolution of data focus on providing humans with deltas that include loads of information. In this work, we envision a processing model that recommends evolution measures taking into account particular challenges, such as relatedness, transparency, diversity, fairness and anonymity. We target at supporting humans with complementary measures that offer high-level overviews of the changes to help them understand how data of interest evolves.

During the last decade, the number of users who look for health-related information has impressively increased. On the other hand, health professionals have less and less time to recommend useful sources of such information online to their patients. To this direction, we target at streamlining the process of providing useful online information to patients by their caregivers and improving as such the opportunities that patients have to inform themselves online about diseases and possible treatments. Using our system, relevant and high quality information is delivered to patients based on their profile, as represented in their personal healthcare record data, facilitating an easy interaction by minimizing the necessary manual effort. Specifically, in this paper, we propose a model for group recommendations following the collaborative filtering approach. Since in collaborative filtering is crucial to identify the correct set of similar users for a user in question, in addition to the traditional ratings, we pay particular attention on how to exploit healthrelated information for computing similarities between users. Our special focus is on providing valuable suggestions to a caregiver who is responsible for a group of users. We interpret valuable suggestions as suggestions that are both highly related and fair to the users of the group. In this line, we propose an algorithm for identifying the top-z most valuable recommendations, and present its implementation in MapReduce.

Entity resolution aims to identify descriptions of the same entity within or across knowledge bases. In this work, we present the Minoan ER platform for resolving entities described by linked data in the Web (e.g., in RDF). To reduce the required number of comparisons, Minoan ER performs blocking to place similar descriptions into blocks and executes comparisons to identify matches only between descriptions within the same block. Moreover, it explores in a pay-as-you-go fashion any intermediate results of matching to obtain similarity evidence of entity neighbors and discover new candidate description pairs for resolution.

Entity resolution constitutes a crucial task for many applications, but has an inherently quadratic complexity. Typically, it scales to large volumes of data through blocking: similar entities are clustered into blocks so that it suffices to perform comparisons only within each block. Meta-blocking further increases efficiency by cleaning the overlapping blocks from unnecessary comparisons. However, even Meta-blocking can be time-consuming: applying it to blocks with 7.4 million entities and 2.21011 comparisons takes almost 8 days on a modern high-end server. In this paper, we parallelize Meta-blocking based on MapReduce. We propose a simple strategy that explicitly creates the core concept of Meta-blocking, the blocking graph. We then describe an advanced strategy that creates the blocking graph implicitly, reducing the overhead of data exchange. We also introduce a load balancing algorithm that distributes the computationally intensive workload evenly among the available compute nodes. Our experimental analysis verifies the superiority of our advanced strategy and demonstrates an almost linear speedup for all meta-blocking techniques with respect to the number of available nodes.

In the Web of data, entities are described by interlinked data rather than documents on the Web. In this work, we focus on entity resolution in the Web of data, i.e., identifying descriptions that refer to the same real-world entity. To reduce the required number of pairwise comparisons, methods for entity resolution perform blocking as a pre-processing step. A blocking technique places similar entity descriptions into blocks and executes comparisons only between descriptions within the same block. We experimentally evaluate blocking techniques proposed for the Web of data and present dataset characteristics that determine the effectiveness and efficiency of such methods. Furthermore, we analyze the characteristics of the missed matching entity descriptions and examine different types of links that blocking techniques can potentially identify.

Top-k is a well-studied problem in the literature, due to its wide spectrum of applications, like information retrieval, database querying, Web search and data mining. In the big data era, the volume of the data and their velocity, call for efficient parallel solutions that overcome the restricted resources of a single machine. Our motivating application is recommenders, which typically deal with big numbers of users and items, but other applications might benefit as well, like keyword search. In this paper, we propose a parallel top-k MapReduce algorithm that, unlike existing MapReduce solutions, manages to handle cases in which the k results do not fit in memory.

Nowadays, WWW brings overwhelming variety of choices to consumers. Recommendation systems facilitate the selection by issuing recommendations to them. Recommendations for users, or groups, are determined by considering users similar to the users in question. Scanning the whole database for locating similar users, though, is expensive. Existing approaches build cluster models by employing full-dimensional clustering to find sets of similar users. As the datasets we deal with are high-dimensional and incomplete, full-dimensional clustering is not the best option. To this end, we explore the fault-tolerant subspace clustering approach. We extend the concept of fault tolerance to density-based subspace clustering, and to speed up our algorithms, we introduce the significance threshold for considering only promising dimensions for subspace extension. Moreover, as we potentially receive a multitude of users from subspace clustering, we propose a weighted ranking approach to refine the set of like-minded users. Our experiments on real movie datasets show that the diversification of the similar users that the subspace clustering approaches offer results in better recommendations compared to traditional collaborative filtering and full-dimensional clustering approaches.

Typically, recommendations are computed by considering users similar to the user in question. However, scanning the whole database of users for locating similar users is expensive. Existing approaches build user profiles by employing full-dimensional clustering to find sets of similar users. As the datasets we deal with are high-dimensional and incomplete, full-dimensional clustering is not the best option. To this end, we explore the fault tolerance subspace clustering approach that detects clusters of similar users in subspaces of the original feature space and also allows for missing values. Our experiments on real movie datasets show that the diversification of the similar users through subspace clustering results in better recommendations comparing to traditional collaborative filtering and full dimensional clustering approaches.

This paper studies the nearest keyword (NK) problem on XML documents. In general, the dataset is a tree where each node is associated with one or more keywords. Given a node q and a keyword w, an NK query returns the node that is nearest to q among all the nodes associated with w. NK search is not only useful as a stand-alone operator but also as a building brick for important tasks such as XPath query evaluation and keyword search. We present an indexing scheme that answers NK queries efficiently, in terms of both practical and worst-case performance. The query cost is provably logarithmic to the number of nodes carrying the query keyword. The proposed scheme occupies space linear to the dataset size, and can be constructed by a fast algorithm. Extensive experimentation confirms our theoretical findings, and demonstrates the effectiveness of NK retrieval as a primitive operator in XML databases.

Preferences have been traditionally studied in philosophy, psychology, and economics and applied to decision making problems. Recently, they have attracted the attention of researchers in other fields, such as databases where they capture soft criteria for queries. Databases bring a whole fresh perspective to the study of preferences, both computational and representational. From a representational perspective, the central question is how we can effectively represent preferences and incorporate them in database querying. From a computational perspective, we can look at how we can efficiently process preferences in the context of database queries. Several approaches have been proposed but a systematic study of these works is missing. The purpose of this survey is to provide a framework for placing existing works in perspective and highlight critical open challenges to serve as a springboard for researchers in database systems. We organize our study around three axes: preference representation, preference composition, and preference query processing.

Keyword-based search in relational databases allows users to discover relevant information without knowing the database schema or using complicated queries. However, such searches may return an overwhelming number of results, often loosely related to the user intent. In this paper, we propose personalizing keyword database search by utilizing user preferences. Query results are ranked based on both their relevance to the query and their preference degree for the user. To further increase the quality of results, we consider two new metrics that evaluate the goodness of the result as a set, namely coverage of many user interests and content diversity. We present an algorithm for processing preference queries that uses the preferential order between keywords to direct the joining of relevant tuples from multiple relations. We then show how to reduce the complexity of this algorithm by sharing computational steps. Finally, we report evaluation results of the efficiency and effectiveness of our approach.

This tutorial provides an overview of the key research results in the area of user preferences from a database perspective. The objective is to survey in a systematic and holistic way a number of approaches for preference representation and composition, querying with preferences and preference learning. Open research problems are also presented.

To provide users with only relevant data from the huge amount of available information, personalization systems utilize preferences to allow users to express their interest on specific pieces of data. Most often, user preferences vary depending on the circumstances. For instance, when with friends, users may like to watch thrillers, whereas, when with their kids, they may prefer to watch cartoons. Contextual preference systems address this challenge by supporting preferences that depend on the values of contextual attributes such as the surrounding environment, time or location. In this paper, we address the problem of finding interesting data items based on contextual preferences that assign interest scores to pieces of data based on context. To this end, we propose a number of pre-processing steps. Instead of pre-computing scores for all data items under all potential context states, we exploit the hierarchical nature of context attributes to identify representative context states. Furthermore, we introduce a method for grouping preferences based on the similarity of the scores that they produce. This method uses a bitmap representation of preferences and scores with various levels of precision that lead to approximate rankings with different degrees of accuracy. We evaluate our approach using both real and synthetic data sets and present experimental results showing the quality of the scores attained using our methods.

To handle the overwhelming amount of information currently available, personalization systems allow users to specify the information that interests them through preferences. Most often, users have different preferences depending on context. In this paper, we introduce a model for expressing such contextual preferences. Context is modeled as a set of multidimensional attributes. We formulate the context resolution problem as the problem of (a) identifying those preferences that qualify to encompass the context state of a query and (b) selecting the most appropriate among them. We also propose an algorithm for context resolution that uses a data structure, called the profile tree, that indexes preferences based on their associated context. Finally, we evaluate our approach from two perspectives: usability and performance.

Personalization systems exploit preferences for providing users with only relevant data from the huge volume of information that is currently available. We consider preferences that dependent on context, such as the location of the user. We model context as a set of attributes, each taking values from hierarchical domains. Often, the context of the query may be too specific to match any of the given preferences. In this paper, we consider possible expansions of the query context produced by relaxing one or more of its context attributes. A hierarchical attribute may be relaxed upwards by replacing its value by a more general one, downwards by replacing its value by a set of more specific values or sideways by replacing its value by sibling values in the hierarchy. We present an algorithm based on a prefix-based representation of context for identifying the preferences whose context matches the relaxed context of the query and some initial performance results.