Adaptive and personalized systems increasingly mediate everyday digital experiences, and recent advances in LLMs, NLP, and Generative AI have amplified their reach, from intelligent user interfaces and conversational agents to AR/immersive interactions and autonomous assistants. These methods now support applications in health and well-being, behavior change and persuasion, e-learning and educational games, and group modeling for collaboration and team formation, all of which require increasingly rich and dynamic user models. At the same time, modern pipelines based on data mining, knowledge graphs/linked data, semantic representations, and affective computing raise urgent questions about transparency, privacy, fairness, accountability, and user understanding, reinforced by regulatory expectations such as the GDPR right to explanation. Yet research often optimizes personalization performance without comparable attention to interpretability and human comprehension. This workshop provides a forum for theoretical, methodological, and empirical work that bridges effectiveness and explainability, with emphasis on robust human-centered evaluation and reproducible practices, including benchmarks, datasets, and shared challenges that advance trustworthy personalization in an era of increasingly autonomous AI.

The growing interest of the research community in multimodal recommendation models has introduced significant challenges, most notably how to effectively process and integrate multimodal features into traditional approaches such as collaborative filtering. Despite the efforts, many existing methods achieve only marginal performance gains at the cost of increased model complexity and longer training time. In this paper, we present MMGCF (MultiModal Graph Collaborative Filtering), a recommendation model that integrates an efficient collaborative filtering backbone with pre-trained multimodal item features, thereby combining the advantages of multimodal representations with negligible additional model complexity. Through extensive experiments, we evaluate MMGCF against state-of-the-art recommendation models. The results demonstrate that our model achieves significant performance gains with minimum overhead, maintaining competitive training times. Our source code is available at https://github.com/swapUniba/MMGCF

Abstract In this paper, we present a methodology to provide users with knowledge-aware recommendations based on the fusion of multimodal item embeddings. Our approach relies on the intuition that each modality ( i.e., graph, text, video, images, etc.) emphasizes different characteristics and nuances of the items, so it is necessary that a comprehensive knowledge-aware recommender system (KARS) encodes and exploits all the different data sources that are available in a specific domain. Accordingly, we design a multimodal KARS architecture based on a deep neural network that: (a) learns a representation of each uni-modal feature ( i.e., description, trailers, covers, audio signals, and so on) through an appropriate encoder; (b) exploits self-attention and cross-attention to fuse the different sources and refine the embeddings; (c) returns a prediction score which represents user’s interest in the item, which is finally used to generate a top-k recommendation list. In the evaluation, we carried out experiments against two datasets, and the results showed that our approach overcame several baselines for multimodal and knowledge-aware recommendations, thus confirming the intuitions behind this work.

Conversational Recommender Systems (CRSs) facilitate item discovery through multi-turn dialogues that elicit user preferences via natural language interaction. This field has gained significant attention following advancements in Natural Language Processing (NLP) enabled by Large Language Models (LLMs). However, current CRS research remains constrained by datasets with fundamental limitations. Human-generated datasets suffer from inconsistent dialogue quality, limited domain expertise, and insufficient scale for real-world application, while synthetic datasets created with proprietary LLMs ignore the diversity of real-world user behavior and present significant barriers to accessibility and reproducibility. The development of effective CRSs depends critically on addressing these deficiencies.To this end, we present DistillRecDial, a novel conversational recommendation dataset generated through a knowledge distillation pipeline that leverages smaller, more accessible open LLMs. Crucially, DistillRecDial simulates a range of user types with varying intentions, preference expression styles, and initiative levels, capturing behavioral diversity that is largely absent from prior work. Human evaluation demonstrates that our dataset significantly outperforms widely adopted CRS datasets in dialogue coherence and domain-specific expertise, indicating its potential to advance the development of more realistic and effective conversational recommender systems.

The last few years have seen an increasing interest of the RecSys community in the multimodal recommendation research field, as shown by the numerous contributions proposed in the literature. Our paper falls in this research line, as we released a multimodal extension of three state-of-the-art datasets (MovieLens-1M, DBbook, Last.fm-2K) in the movie, book, music recommendation domains, respectively. Although these datasets have been widely adopted for classical recommendation tasks (e.g., collaborative filtering), their use in multimodal recommendation has been hindered by the absence of multimodal information. To fill this gap, we have manually collected multimodal item raw files from different modalities (text, images, audio, and video, when available) for each dataset.Specifically, we have collected, for MovieLens-1M, movie plots (textual information), movie posters (images) and movie trailers (audio and video); for Last.fm-2K, we have collected, for each artist, the tags provided by users (textual information), the most popular album covers (images), and the most popular songs (audio); finally, for DBbook we have collected book abstracts (textual information) and book covers (image). We encoded all this information using state-of-the-art feature encoders, and we released the extended datasets, which include the mappings to the raw multimodal information and the encoded features. Finally, we conduct a benchmark analysis of various recommendation models using MMRec as a multimodal recommendation framework. Our results show that multimodal information can further enhance the quality of recommendations in these domains compared to single collaborative filtering. We release the multimodal version of such datasets to foster this research line, including links to download the raw multimodal files and the encoded item features.

Recommender systems (RSs) have become increasingly versatile, finding applications across diverse domains.Large Language Models (LLMs) significantly contribute to this advancement since the vast amount of knowledge embedded in these models can be easily exploited to provide users with high-quality recommendations.However, current RSs based on LLMs have room for improvement.As an example, knowledge injection techniques can be used to finetune LLMs by incorporating additional data, thus improving their performance on downstream tasks.In a recommendation setting, these techniques can be exploited to incorporate further knowledge, which can result in a more accurate representation of the items.Accordingly, in this paper, we propose a pipeline for knowledge injection specifically designed for RS.First, we incorporate external knowledge by drawing on three sources: (a) knowledge graphs; (b) textual descriptions; (c) collaborative information about user interactions.Next, we lexicalize the knowledge, and we instruct and fine-tune an LLM, which can easily return a list of recommendations.Extensive experiments on movie, music, and book datasets validate our approach.Moreover, the experiments showed that knowledge injection is particularly needed in domains (i.e., music and books) where the encoded knowledge within LLMs may not be suitable for recommendation tasks, even if such content was used during the training of the model.This finding points to several promising future research directions.

In this paper, we propose a recommendation model that exploits a graph augmentation technique based on Large Language Models (LLMs) to enrich the information encoded in its underlying Knowledge Graph (KG).Our work relies on the assumption that the triples encoded in a KG can often be noisy or incomplete, and this may lead to sub-optimal modeling of both the characteristics of items and the users' preferences.In this setting, graph augmentation can be a suitable solution to improve the quality of the data model and provide users with high-quality recommendations.Accordingly, in this work, we align with this research line and propose GAL-KARS (Graph Augmentation with LLMs for Knowledge-Aware Recommender Systems).In our framework, we start from a KG, and we design some prompts for querying an LLM and augmenting the graph by incorporating: (a) further features describing the items; (b) further nodes describing the preferences of the users, obtained by reasoning over the items they like.The resulting KG is then passed through a Knowledge Graph Encoder that learns users' and items' embeddings based on the augmented KG.These embeddings are finally used to train a recommendation model and provide users with personalized suggestions.As shown in the experimental session, graph augmentation based on LLMs can significantly improve the predictive accuracy of our recommendation model, thus confirming the effectiveness of the model and the validity of our intuitions.

Recommender systems, though widely used, often struggle to engage users effectively. While deep learning methods have enhanced connections between users and items, they often neglect the user’s perspective. Knowledge-based approaches, utilizing knowledge graphs, offer semantic insights and address issues like knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. More recently, neural-symbolic systems, combining data-driven and symbolic techniques, show promise in recommendation systems, especially when used with knowledge graphs. Moreover, content features become vital in conversational recommender systems, which demand multi-turn dialogues. Recent literature highlights increasing interest in this area, particularly with the emergence of Large Language Models (LLMs), which excel in understanding user queries and generating recommendations in natural language. Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop aims to disseminate advancements and discuss about challenges and opportunities.

Recommender systems can significantly benefit from the availability of pre-trained large language models (LLMs), which can serve as a basic mechanism for generating recommendations based on detailed user and item data, such as text descriptions, user reviews, and metadata. On the one hand, this new generation of LLM-based recommender systems paves the way for dealing with traditional limitations, such as cold-start and data sparsity. Still, on the other hand, this poses fundamental challenges for their accountability. Reproducing experiments in the new context of LLM-based recommender systems is challenging for several reasons. New approaches are published at an unprecedented pace, which makes difficult to have a clear picture of the main protocols and good practices in the experimental evaluation. Moreover, the lack of proper frameworks for LLM-based recommendation development and evaluation makes the process of benchmarking models complex and uncertain.

Given the rising global concerns about healthy nutrition and environmental sustainability, individuals need more and more support in making good choices concerning their daily meals. To this end, in this paper we introduce HeaSE, a framework for Healthy And Sustainable Eating. Given an input recipe, HeaSE identifies healthier and more sustainable meals by exploiting retrieval techniques and large language models. The framework works in two steps. First, it uses food retrieval strategies based on macro-nutrient information to identify candidate alternative meals. This ensures that the substitutions maintain a similar nutritional profile. Next, HeaSE employs large language models to re-rank these potential replacements while considering factors beyond just nutrition, such as the recipe’s environmental impact. In the experimental evaluation, we showed the capabilities of LLMs in identifying more sustainable and healthier alternatives within a set of candidate options. This highlights the potential of these models to guide users towards food choices that are both nutritious and environmentally responsible.

In this paper, we present a strategy to provide users with explainable cross-domain recommendations (CDR) that exploits large language models (LLMs). Generally speaking, CDR is a task that is hard to tackle, mainly due to data sparsity issues. Indeed, CDR models require a large amount of data labeled in both source and target domains, which are not easy to collect. Accordingly, our approach relies on the intuition that the knowledge that is already encoded in LLMs can be used to more easily bridge the domains and seamlessly provide users with personalized cross-domain suggestions.

This work investigates the path toward green recommender systems by examining the impact of data reduction on both model performance and carbon footprint. In the pursuit of developing energy-efficient recommender systems, we investigated whether and how reducing the training data impacts the performances of several representative recommendation models. In order to obtain a fair comparison, all the models were run based on the implementations available in a popular recommendation library, i.e., RecBole, and used the same experimental settings. Results indicate that: (a) data reduction can be a promising strategy to make recommender systems more sustainable, at the cost of a lower accuracy; (b) training recommender systems with less data makes the suggestions more diverse and less biased. Overall, this study contributes to the ongoing discourse on the development of recommendation models that meet the principles of SDGs, laying the groundwork for the adoption of more sustainable practices in the field.

Conversational Recommender Systems (CRS) have recently drawn attention due to their capacity of delivering personalized recommendations through multi-turn natural language interactions. In this paper, we fit into this research line and we introduce a Knowledge-Aware Sequential Conversational Recommender System (KASCRS) that exploits transformers and knowledge graph embeddings to provide users with recommendations in a conversational setting.

In this paper, we introduce a Knowledge-aware Recommender System (KARS) based on Graph Neural Networks that exploit pre-trained content-based embeddings to improve the representation of users and items. Our approach relies on the intuition that textual features can describe the items in the catalog from a different point of view, so they are worth to be exploited to provide users with more accurate recommendations. Accordingly, we used encoding techniques to learn a pre-trained representation of the items in the catalogue based on textual content, and we used these embeddings to feed the input layer of a KARS based on GCNs. In this way, the GCN is able to encode both the knowledge coming from the unstructured content and the structured knowledge provided by the KG (ratings and item descriptive properties). As shown in our experiments, the exploitation of pre-trained embeddings improves the predictive accuracy of the KARS, which overcomes all the baselines we considered in several experimental settings.

Abstract This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a recommender system. The hallmark of our strategy lies in the fact that natural language justifications are adapted to the different contextual situations in which the items will be consumed. In particular, our strategy relies on the following intuition: Just like the selection of the most suitable item is influenced by the contexts of usage, a justification that supports a recommendation should vary as well. As an example, depending on whether a person is going out with her friends or her family, a justification that supports a restaurant recommendation should include different concepts and aspects. Accordingly, we designed a pipeline based on distributional semantics models to generate a vector space representation of each context. Such a representation, which relies on a term-context matrix, is used to identify the most suitable review excerpts that discuss aspects that are particularly relevant for a certain context. The methodology was validated by means of two user studies, carried out in two different domains (i.e., movies and restaurants). Moreover, we also analyzed whether and how our justifications impact on the perceived transparency of the recommendation process and allow the user to make more informed choices. As shown by the results, our intuitions were supported by the user studies.

Abstract In this paper, we present a knowledge-aware recommendation model based on neuro-symbolic graph embeddings that encode first-order logic rules. Our approach is based on the intuition that is the basis of neuro-symbolic AI systems: to combine deep learning and symbolic reasoning in one single model, in order to take the best out of both the paradigms. To this end, we start from a knowledge graph (KG) encoding information about users, ratings, and descriptive properties of the items and we design a model that combines background knowledge encoded in logical rules mined from the KG with explicit knowledge encoded in the triples of the KG itself to obtain a more precise representation of users and items. Specifically, our model is based on the combination of: (i) a rule learner that extracts first-order logic rules based on the information encoded in the knowledge graph; (ii) a graph embedding module, that jointly learns a vector space representation of users and items based on the triples encoded in the knowledge graph and the rules previously extracted; (iii) a recommendation module that uses the embeddings to feed a deep learning architecture that provides users with top-k recommendations. In the experimental section, we evaluate the effectiveness of our strategy on three datasets, and the results show that the combination of knowledge graph embeddings and first-order logic rules led to an improvement in the predictive accuracy and in the novelty of the recommendations. Moreover, our approach overcomes several competitive baselines, thus confirming the validity of our intuitions.

Abstract Users of online recipe websites tend to prefer unhealthy foods. Their popularity undermines the healthiness of traditional food recommender systems, as many users lack nutritional knowledge to make informed food decisions. Moreover, the presented information is often unrelated to nutrition or difficult to understand. To alleviate this, we present a methodology to generate natural language justifications that emphasize the nutritional content, health risks, or benefits of recommended recipes. Our framework takes a user and two recipes as input and produces an automatically generated natural language justification as output, based on the user’s characteristics and the recipes’ features, following a knowledge-based recommendation approach. We evaluated our methodology in two crowdsourcing studies. In Study 1 ( $$N=502$$ N = 502 ), we compared user food choices for two personalized recommendation approaches, based on either a (1) single-style justification or (2) comparative justification was shown, using a no justification baseline. The recommendations were either popularity-based or health-aware, the latter based on the health and nutritional needs of the user. We found that comparative justification styles were effective in supporting choices for our health-aware recommendations, confirming the impact of our methodology on food choices. In Study 2 ( $$N=504$$ N = 504 ), we used the same methodology to compare the effectiveness of eight different comparative justification strategies. We presented pairs of recipes twice to users: once without and once with a pairwise justification. Results indicated that justifications led to significantly healthier choices for first course meals, while strategies that compared food features and emphasized health risks, benefits, and a user’s lifestyle were most effective, catering to health-related choice motivations.

Recommender systems have become ubiquitous in daily life, but their limitations in interacting with human users have become evident. Deep learning approaches have led to the development of data-driven algorithms that identify connections between users and items, but they often miss a critical actor in the loop - the end-user. Knowledge-based approaches are gaining attention due to the availability of knowledge-graphs, such as DBpedia and Wikidata, which provide semantics-aware information on different knowledge domains. These approaches are being used for recommendation and challenges such as knowledge graph embeddings, hybrid recommendation, and interpretable recommendation. Moreover, the emergence of neural-symbolic systems, which combine data-driven and symbolic methods, can significantly improve recommendation systems. A growing number of research papers on such topics demonstrate the growing interest and research potential of these systems. Furthermore, content features become crucial when interaction requires it. The development of conversational recommender systems presents new challenges, as they require multi-turn dialogues between users and systems, blurring the line between recommendation and retrieval. Evaluation of these systems goes beyond simple accuracy metrics and is hampered by the limited availability of datasets. While research and development into conversational recommender systems has been less prominent in the past, recent literature shows growing interest and potential for these systems.

Reproducibility is an important requirement for scientific progress, and the lack of reproducibility for a large amount of published research can hinder the progress over the state-of-the-art. This concerns several research areas, and recommender systems are witnessing the same reproducibility crisis. Even solid works published at prestigious venues might not be reproducible for several reasons: data might not be public, source code for recommendation algorithms might not be available or well documented, and evaluation metrics might be computed using parameters not explicitly provided. In addition, recommendation pipelines are becoming increasingly complex due to the use of deep neural architectures or representations for multimodal side information involving text, images, audio, or video. This makes the reproducibility of experiments even more challenging.

In this paper, we present a comparative analysis of the trade-off between the performance of state-of-the-art recommendation algorithms and their environmental impact. In particular, we compared 18 popular recommendation algorithms in terms of both performance metrics (i.e., accuracy and diversity of the recommendations) as well as in terms of energy consumption and carbon footprint on three different datasets. In order to obtain a fair comparison, all the algorithms were run based on the implementations available in a popular recommendation library, i.e., RecBole, and used the same experimental settings. The outcomes of the experiments showed that the choice of the optimal recommendation algorithm requires a thorough analysis, since more sophisticated algorithms often led to tiny improvements at the cost of an exponential increase of carbon emissions. Through this paper, we aim to shed light on the problem of carbon footprint and energy consumption of recommender systems, and we make the first step towards the development of sustainability-aware recommendation algorithms.

Knowledge-aware algorithms represent one of the most innovative research directions in the area of recommender systems. The use of different types of content representation requires new methods to extract descriptive features to adopt in the recommendation process. The literature on knowledge-aware recommender systems is actually rich and constantly evolving in terms of both techniques and software libraries to implement them. This makes also difficult to define reproducible recommendation pipelines, making the accountability of recommender systems a challenge. This tutorial aims to discuss the most recent trends in the area of knowledge-aware recommender systems, including novel representation methods for textual content, and discuss how to implement reproducible pipelines for knowledge-aware recommender systems. We pursue our goals by using a comprehensive Python framework called ClayRS1 to deal with knowledge-aware recommender systems. We would like to provide: (i) common ground for researchers and practitioners interested in the latest knowledge-aware techniques for user modeling and recommender systems; (ii) a practical way for implementing the whole recommendation pipeline, ranging from the content processing for text to the generation of recommendations and the evaluation of their performance.

In this paper, we present a strategy to provide users with knowledge-aware recommendations based on the combination of graph neural networks and sentence encoders. In particular, our approach relies on the intuition that different data sources (i.e., structured data available in a knowledge graph and unstructured data, such as textual content) provide complementary information and can equally contribute to learn an accurate item representation. Accordingly, we first exploited graph neural networks to encode both collaborative features, such as the interactions between users and items, and structured properties of the items. Next, we used a sentence encoder that relies on transformers to learn a representation based on textual content describing the items. Finally, these embeddings are combined by exploiting a deep neural network where both self-attention and cross-attention mechanisms are used to learn the relationships between the initial embeddings and to further refine the representation. Such a neural network provides as output a prediction of users’ interest in the items, which is used to return a top-k recommendation list. In the experimental evaluation, we carried out an experiment against two datasets, and the results showed that our approach overcame several competitive baselines.

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content. Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered when considering software libraries. This makes it difficult to easily exploit advanced content representation and implement replicable experimental protocols. Accordingly, this work aims to fill in these gaps by introducing ClayRS, an end-to-end framework for replicable knowledge-aware recommender systems. ClayRS provides researchers and practitioners with the most recent state-of-the-art methodologies to build knowledge-aware content representations and also includes methods to exploit these representations in content-based recommendation algorithms. Finally, the structure of the framework also allows for building replicable pipelines to push forward the current research in the area and to develop accountable recommender systems.

In the last few years, a renewed interest of the research community in conversational recommender systems (CRSs) has been emerging. This is likely due to the massive proliferation of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language utterances. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they still remain at an early stage in terms of their recommendation capabilities via a conversation. In addition, we have been witnessing the advent of increasingly precise and powerful recommendation algorithms and techniques able to effectively assess users’ tastes and predict information that may be of interest to them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and neglect the huge amount of knowledge, both structured and unstructured, describing the domain of interest of a recommendation engine. Although very effective in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move in the direction of providing novel and diverse results as well as generating explanations for recommended items. Knowledge-aware side information becomes crucial when a conversational interaction is implemented, in particular for preference elicitation, explanation, and critiquing steps.

The FinRec workshop series offers a central forum for the study and discussion of the domain-specific aspects, challenges, and opportunities of RecSys and other related technologies in the financial services domain. Six years after the second edition of the workshop, the recent advances in the area of personalization and recommendation in financial services fostered the need for a new workshop aiming at bringing together researchers and practitioners working in financial services-related areas. Accordingly, the third edition of the event aims to: (1) understand and discuss open research challenges, (2) provide an overview of existing technologies using recommender systems in the financial services domain, and (3) provide an interactive platform for information exchange between industry and academia.

In this paper, we present a knowledge-aware recommendation framework based on neuro-symbolic graph embeddings that encode first-order logical (FOL) rules. In particular, our workflow starts from a knowledge graph (KG) encoding user preferences (based on explicit ratings [13]) and item properties. Next, knowledge-aware recommendation are obtained through the combination of three modules: (i) a rule learner, that extracts FOL rules from the KG; (ii) a graph embedding module, that learns the embeddings of users and items based on the triples of the KG and the FOL rules previously extracted; (iii) a recommendation module that uses the embeddings to feed a deep learning architecture. In the experimental session, we evaluate the effectiveness of our strategy on two datasets and the results show that the combination of KG embeddings and FOL rules led to an improvement in the accuracy and in the novelty of the recommendations.

In the traditional categorization of recommendation techniques, content-based methods are often considered as an alternative to the most widely adopted collaborative filtering approaches. Content-based recommender systems suggest items similar to a user profile by matching attributes obtained by processing textual content. In order to deal with natural language ambiguity, semantics-aware representations can help to build more precise representations of users and items, and, in turn, to generate better recommendations. This tutorial (i) presents the most recent trends in the area of semantics-aware content-based recommender systems, including novel representation methods based on knowledge graphs and embedding techniques, (ii) discusses how to implement reproducible pipelines for semantics-aware recommender systems, and (iii) presents a new and comprehensive Python framework called ClayRS to deal with semantics-aware recommender systems.

In the last few years, a renewed interest of the research community on conversational recommender systems (CRSs) is emerging. This is probably due to the great diffusion of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language messages. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they are still at an early stage on offering recommendation capabilities by using the conversational paradigm.

In this paper, we present a hybrid recommendation framework based on the combination of graph embeddings and contextual word representations. Our approach is based on the intuition that each of the above mentioned representation models heterogeneous (and equally important) information, that is worth to be taken into account to generate a recommendation. Accordingly, we propose a strategy to combine both the features, which is based on the following steps: first, we separately generate graph embeddings and contextual word representations by exploiting state-of-the-art techniques. Next, these embeddings are used to feed a deep architecture that learns a hybrid representation based on the combination of the single groups of features. Finally, we exploit the resulting embedding to identify suitable recommendations. In the experimental session, we evaluate the effectiveness of our strategy on two datasets and results show that the use of a hybrid representation leads to an improvement of the predictive accuracy. Moreover, our approach overcomes several competitive baselines, thus confirming the validity of this work.

Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user’s characteristics and the recipes’ features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe’s food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices.

In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling ; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process. Basically, the system allows the users to formulate natural language requests related to their information needs. Such needs are roughly classified in two groups: quantified self-related needs (e.g., Did I sleep enough? Am I extrovert? ) and personalized access to online services (e.g., Play a song I like ). The intent recognition strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query. In the experimental evaluation, we evaluated both qualitative (users’ acceptance of the system, usability) as well as quantitative (time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that MyrrorBot can improve the way people access online services and applications. This leads to a more effective interaction and paves the way for further development of our system.

Adaptive and personalized systems have become pervasive technologies which are gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interact every day with algorithms that help us in several scenarios, ranging from services that suggest us music to be listened to or movies to be watched, to personal assistants able to proactively support us in complex decision-making tasks.

Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits.

Though there are currently no statistics offering a global overview of online hate speech, both social networking platforms and organisations that combat hate speech have recognised that prevention strategies are needed to address this negative online phenomenon. While most cases of online hate speech target individuals on the basis of ethnicity and nationality, incitements to hatred on the basis of religion, class, gender and sexual orientation are increasing. This paper reports the findings of the ‘Italian Hate Map’ project, which used a lexicon-based method of semantic content analysis to extract 2,659,879 Tweets (from 879,428 Twitter profiles) over a period of 7 months; 412,716 of these Tweets contained negative terms directed at one of the six target groups. In the geolocalized Tweets, women were the most insulted group, having received 71,006 hateful Tweets (60.4% of the negative geolocalized tweets), followed by immigrants (12,281 tweets, 10.4%), gay and lesbian persons (12,140 tweets, 10.3%), Muslims (7,465 tweets, 6.4%), Jews (7,465 tweets, 6.4%) and disabled persons (7,230 tweets, 6.1%). The findings provide a real-time snapshot of community behaviours and attitudes against social, ethnic, sexual and gender minority groups that can be used to inform intolerance prevention campaigns on both local and national levels.

In this paper we present a methodology to justify recommendations that relies on the information extracted from users' reviews discussing the available items. The intuition behind the approach is to conceive the justification as a summary of the most relevant and distinguishing aspects of the item, automatically obtained by analyzing its reviews. To this end, we designed a pipeline of natural language processing techniques including aspect extraction, sentiment analysis and text summarization to gather the reviews, process the relevant excerpts, and generate a unique synthesis presenting the main characteristics of the item. Such a summary is finally presented to the target user as a justification of the received recommendation. In the experimental evaluation we carried out a user study in the movie domain (N=141) and the results showed that our approach is able to make the recommendation process more transparent, engaging and trustful for the users.

In this paper we present a methodology to justify the suggestions generated by a recommendation algorithm through the identification of relevant and distinguishing characteristics of the recommended item, automatically extracted by mining users' reviews. Our approach relies on a combination ofnatural language processing and sentiment analysis techniques, and is based on the following steps: (1) a set of users' reviews discussing the recommended item is gathered and analyzed; (2) the distinguishing aspects that characterize the item are extracted and a ranking function is used to identify the most relevant ones; (3) excerpts of the reviews discussing such aspects are extracted and a natural language template is filled in through the aggregation of these sentences. This represents the final output of the algorithm, which is provided to the user as justification of the recommendation she received. In the experimental evaluation, we carried out a user study (N=296, 73.6% male) aiming to investigate the effectiveness of our methodology in two different domains, as movies and books. Results showed that our technique can provide users with rich and satisfying justifications. Moreover, our experiment also showed that the users prefer review-based justifications to other explanation strategies, and this finding further confirmed the effectiveness of the approach.

More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest for the recommendation engine. The aim of knowledge-aware and conversational recommender systems is to go beyond the traditional accuracy goal and to start a new generation of algorithms and interactive approaches which exploit the knowledge encoded in ontological and logic-based knowledge bases, knowledge graphs as well as the semantics emerging from the analysis and exploitation of semi-structured textual sources.

In this paper we propose a multi-criteria recommender system based on collaborative filtering (CF) techniques, which exploits the information conveyed by users' reviews to provide a multi-faceted representation of users' interests.

In this article we propose a hybrid recommendation framework based on classification algorithms as Random Forests and Naive Bayes. We fed the framework with several heterogeneous groups of features, and we investigate to what extent features gathered from the Linked Open Data (LOD) cloud (as the genre of a movie or the writer of a book)) as well as graph-based features calculated on the ground of the tripartite representation connecting users, items and properties in the LOD cloud impact on the overall accuracy of the recommendations. In the experimental session we evaluate the effectiveness of our framework on varying of different groups of features, an results show that both LOD-based and graph-based features positively affect the overall performance of the algorithm, especially in highly sparse recommendation scenarios.

In this paper we investigate the effectiveness of Recurrent Neural Networks (RNNs) in a top-N content-based recommendation scenario. Specifically, we propose a deep architecture which adopts Long Short Term Memory (LSTM) networks to jointly learn two embeddings representing the items to be recommended as well as the preferences of the user. Next, given such a representation, a logistic regression layer calculates the relevance score of each item for a specific user and we returns the top-N items as recommendations.

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2016 workshop provides a dedicated venue for papers dedicated to all aspects of content-based recommendation.

In this paper we present ExpLOD, a framework which exploits the information available in the Linked Open Data (LOD) cloud to generate a natural language explanation of the suggestions produced by a recommendation algorithm. The methodology is based on building a graph in which the items liked by a user are connected to the items recommended through the properties available in the LOD cloud. Next, given this graph, we implemented some techniques to rank those properties and we used the most relevant ones to feed a module for generating explanations in natural language. In the experimental evaluation we performed a user study with 308 subjects aiming to investigate to what extent our explanation framework can lead to more transparent, trustful and engaging recommendations. The preliminary results provided us with encouraging findings, since our algorithm performed better than both a non-personalized explanation baseline and a popularity-based one.

The ever increasing interest in semantic technologies and the availability of several open knowledge sources have fueled recent progress in the field of recommender systems. In this paper we feed recommender systems with features coming from the Linked Open Data (LOD) cloud - a huge amount of machine-readable knowledge encoded as RDF statements - with the aim of improving recommender systems effectiveness. In order to exploit the natural graph-based structure of RDF data, we study the impact of the knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation algorithm. In more detail, we investigate whether the integration of LOD-based features improves the effectiveness of the algorithm and to what extent the choice of different feature selection techniques influences its performance in terms of accuracy and diversity. The experimental evaluation on two state of the art datasets shows a clear correlation between the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, the graph-based algorithm leveraging LOD-based features is able to overcome several state of the art baselines, such as collaborative filtering and matrix factorization, thus confirming the effectiveness of the proposed approach.

This paper presents the results of The Italian Hate Map, a research project aiming to monitor the level of intolerance of the Italian country by mining the content posted on social networks. Within the project, a pipeline of algorithms for data extraction, semantic processing, sentiment analysis and content classification has been defined to process huge amounts of Tweets and to build a map of the most at-risk areas, thus identifying the Italian communities tending to have a more intolerant behavior. The outcomes resulting from the analysis of the maps confirmed the insight that the adoption of semantic content analysis techniques can be very useful to create value from the rough content available on the Web, and to go one step further in understanding very complex phenomena by modeling offline behavior of the communities on the ground of their online behavior on social networks.

In the last years, hundreds of social networks sites have been launched with both professional (e.g., LinkedIn) and non-professional (e.g., MySpace, Facebook) orientations. This resulted in a renewed information overload problem, but it also provided a new and unforeseen way of gathering useful, accurate and constantly updated information about user interests and tastes. Content-based recommender systems can leverage the wealth of data emerging by social networks for building user profiles in which representations of the user interests are maintained.

The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice within the scientific community. The reason is twofold: first, its very clean and solid formalism allows us to represent objects in a vector space and to perform calculations on them. On the other hand, as proved by many contributions, its simplicity does not hurt the effectiveness of the model. Although Information Retrieval and Information Filtering undoubtedly represent two related research areas, the use of VSM in Information Filtering is much less analzyed.

Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. This paper describes a content-based recommender system, called FIRSt, that integrates user generated content (UGC) with semantic analysis of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and dynamic content are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests, often hidden behind keywords. The proposed approach has been evaluated in the domain of cultural heritage personalization.

Recommender systems (RSs) proved to make easier the task of accessing relevant information in a broad range of domains. In content-based RSs, preferences on content items expressed by users turned out to be reliable indicators to suggest and filter interesting contents. Item representation plays a key role in content-based RSs, thus choosing proper facets to represent items is a fundamental task for deploying effective RSs. Contextual facets are often marginally relevant to predict user preferences, but in some domains disregarding contextual facets makes recommendations useless. This paper proposes a strategy to improve the effectiveness of a content-based RS that dynamically suggests tours within a museum by exploiting contextual facets such the physical layout of items and the interaction of users with the environment.