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.

The primary goal of Recommender Systems is to suggest the most suitable items to a user, aligning them with the user’s interests and needs. RSs are essential for modern e-commerce, helping users discover content and products by predicting suitable items based on their past behavior. However, their success isn’t just about advanced algorithms. The design of the user interface and a good integration with the human decision-making process are equally crucial. A well-designed interface enhances the user experience and makes recommendations more effective, while a poor interface can lead to frustration. Recognizing this limitation, recent trends in Recommender Systems (RSs) are increasingly focusing on integrating Symbiotic Human-Machine Decision-Making models. These models aim to offer users a dynamic and persuasive interface that helps them better understand and engage with recommendations. This shift is a crucial step toward developing recommender systems that truly connect with users and offer a more enjoyable, trustworthy, explainable, and user-friendly experience. Although early efforts concentrated on creating systems that could proactively predict user preferences and needs, modern RSs also emphasize the importance of providing users with control and transparency over their recommendations. Finding the right balance between proactivity and user control is essential to ensure that the system supports users without being too intrusive, thus improving their overall satisfaction. As Large Language Models (LLMs) become more integrated into recommender systems, the importance of user-centric interfaces and a deep understanding of decision-making becomes even more critical. Effective integration of LLMs requires interfaces that are both visually and cognitively engaging.

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.

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 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.

Recommender systems (RSs) have undoubtedly played a significant role in addressing the information overload problem by efficiently filtering and suggesting relevant items to users. These systems use both explicit and implicit user preferences to filter available data and suggest items that might align with the user’s interests. This can range from recommending movies on a streaming platform based on previous views to suggesting products for purchase based on browsing history. In their early stages, RSs focused on enhancing their algorithmic capabilities to provide accurate recommendations. However, the overemphasis on algorithms resulted in neglecting the human aspect of the user experience. Recognizing this limitation, recent trends in RSs have started to shift their attention toward incorporating Symbiotic Human-Machines Decision Making models. These models aim to provide users with dynamic and persuasive interfaces that empower them to understand and engage better with the recommendations. This shift represents an essential step in creating recommender systems that truly resonate with users and create a more enjoyable, trustable, and user-friendly experience. A crucial aspect of recommender systems’ evolution lies in their proactive nature. Early works focused on designing systems that could proactively anticipate user preferences and needs. While this remains a valuable trait, modern RSs also recognize the importance of giving users control and transparency over their recommendations. Striking the right balance between proactivity and user control ensures that the system supports users without being overly intrusive, thus enhancing their overall satisfaction. These aspects are the main discussion topics of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’23. In this summary, we introduce the workshop’s motivation and view, review its history, and discuss the most critical issues that deserve attention for future research directions.

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.

Abstract The Italian Public Administration (PA) relies on costly manual analyses to ensure the GDPR compliance of public documents and secure personal data. Despite recent advances in Artificial Intelligence (AI) have benefited many legal fields, the automation of workflows for data protection of public documents is still only marginally affected. The main aim of this work is to design a framework that can be effectively adopted to check whether PA documents written in Italian meet the GDPR requirements. The main outcome of our interdisciplinary research is INTREPID (art ficial i elligence for gdp complianc of ublic adm nistration ocuments), an AI-based framework that can help the Italian PA to ensure GDPR compliance of public documents. INTREPID is realized by tuning some linguistic resources for Italian language processing (i.e. SpaCy and Tint) to the GDPR intelligence. In addition, we set the foundations for a text classification methodology to recognise the public documents published by the Italian PA, which perform data breaches. We show the effectiveness of the framework over a text corpus of public documents that were published online by the Italian PA. We also perform an inter-annotator study and analyse the agreement of the annotation predictions of the proposed methodology with the annotations by domain experts. Finally, we evaluate the accuracy of the proposed text classification model in detecting breaches of security.

The constant increase in the amount of data and information available on the Web has made the development of systems that can support users in making relevant decisions increasingly important. Recommender systems (RSs) have emerged as tools to address this task. RSs use the preferences expressed by a user, either explicitly or implicitly, to filter the available information and proactively suggest items that might be of interest to him or her. Although in early works about the topic there was a strong interest in ways to make such systems proactive, user-friendly, and persuasive, over time they became increasingly focused on the algorithmic component solely. However, this trend is gradually being reversed and always more attention is nowadays placed also on Human Decision Making models that focus on supporting the end user in understanding what is being proposed through RSs by using dynamic and persuasive interfaces. A recommender system should be based on valuable strategies for proactively guiding users to items that match their preferences and therefore should put attention on how it is possible to make this process trustable, pleasant, and user-friendly. Such systems, moreover, should take into account psychological, cognitive and emotional aspects to enable personalization that is appropriate not only to the context of use but also to the psychological reactions of the end user. The workshop provides a venue for works that invest in the design of recommender systems which consider users’ experience during the interaction, as well as for works that explore the implications of human-computer interactions with different theories of human decision-making. In this summary, we introduce the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’22, review its history, and discuss the most important topics considered at the workshop.

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.

Recommender systems were originally developed as interactive intelligent systems that can proactively guide users to items that match their preferences. Despite its origin on the crossroads of HCI and AI, the majority of research on recommender systems gradually focused on objective accuracy criteria paying less and less attention to how users interact with the system as well as the efficacy of interface designs from users’ perspectives. This trend is reversing with the increased volume of research that looks beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the ”human side” of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary, we introduce the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’21, review its history, and discuss most important topics considered at the workshop.

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.

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.

As an interactive intelligent system, recommender systems are developed to give recommendations that match users’ preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it is ready to look beyond algorithms, into users’ interactions, decision making processes, and overall experience. The series of workshops on Interfaces and Human Decision Making for Recommender Systems focuses on the ”human side” of recommender systems. The goal of the research stream featured at the workshop is to improve users’ overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems. In this summary, we introduce 7th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’20, review its history, and discuss most important topics considered at the workshop.

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. This workshop will focus on the "human side" of recommender systems research. The workshop goal is to improve users' overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.

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.

As an interactive intelligent system, recommender systems are developed to give recommendations that match users' preferences. Since the emergence of recommender systems, a large majority of research focuses on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users' perspectives. The field has reached a point where it is ready to look beyond algorithms, into users' interactions, decision making processes, and overall experience. his workshop will focus on the "human side" of recommender systems research. The workshop goal is to improve users' overall experience with recommender systems by integrating different theories of human decision making into the construction of recommender systems and exploring better interfaces for recommender systems.

As intelligent interactive systems, recommender systems focus on determining predictions that fit the wishes and needs of users. Still, a large majority of recommender systems research focuses on accuracy criteria and much less attention is paid to how users interact with the system, and in which way the user interface has an influence on the selection behavior of the users. Consequently, it is important to look beyond algorithms. The main goals of the IntRS workshop are to analyze the impact of user interfaces and interaction design, and to explore human interaction with recommender systems from a human decision making perspective. Methodologies for evaluating these aspects are also within the scope of the workshop.

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 recent years we are witnessing a growing spread of social media footprints, as the consequence of the wide use of applications such as Facebook, Twitter or LinkedIn, which allow people to share content that might provide information about personal preferences and aptitudes. Among the traits that can be inferred, empathy is the ability to feel and share another person's emotions and we consider it as a relevant aspect for the profiling and recommendation tasks. We propose a method that predicts its level for the user by exploiting her social media data and using linear regression algorithms. The results show which are the most relevant correlations among the different groups of user's features and the empathy level predicted.

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.

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.

This paper presents T-RecS (Temporal analysis of Recommender Systems conference proceedings), a framework that supplies services to analyze the Recommender Systems Conference proceedings from the first edition, held in 2007, to the last one, held in 2015, under a temporal point of view. The idea behind T-RecS is to identify linguistic phenomena that reflect some interesting variations for the research community, such as topic drift, or how the correlation between two terms changed over time, or how similarity between two authors evolved over time.

The 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) is taking place in Boston on September 16th, 2016 in conjunction with the ACM RecSys 2016 conference. The workshop focuses on the acquisition and usage of emotions and personality as user-centric aspects of personalization.

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.

“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players.

A primary function of recommender systems is to help their users to make better choices and decisions. The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effective and efficient human decision making in different types of recommendation scenarios. The submitted papers discuss a wide range of topics from core algorithmic issues to the management of the human computer interaction.

The primary goal of the workshop is to showcase cutting edge research on the intersection of Recommender Systems and Semantic Technologies, by taking the best of the two worlds. This combination may provide the RecSys community with important scenarios where the potential of Semantic Technologies can be effectively exploited into systems performing complex tasks, such as recommendation engines processing Big Data.

Recommender systems have already proved to be valuable for coping with the information overload problem in several application domains. They provide people with suggestions for items which are likely to be of interest for them; hence, a primary function of recommender systems is to help people make good choices and decisions. However, most previous research has focused on recommendation techniques and algorithms, and less attention has been devoted to the decision making processes adopted by the users and possibly supported by the system. There is still a gap between the importance that the community gives to the assessment of recommendation algorithms and the current range of ongoing research activities concerning human decision making. Different decision-psychological phenomena can influence the decision making of users of recommender systems, and research along these lines is becoming increasingly important and popular. This special issue highlights how the coupling of recommendation algorithms with the understanding of human choice and decision making theory has the potential to benefit research and practice on recommender systems and to enable users to achieve a good balance between decision accuracy and decision effort.

Interacting with a recommender system means to take different decisions such as selecting an item from a recommendation list, selecting a specific item feature value (e.g., camera's size, zoom) as a search criteria, selecting feedback features to be critiqued in a critiquing based recommendation session, or selecting a repair proposal for inconsistent user preferences when interacting with a knowledge-based recommender. In all these situations, users face a decision task. This workshop ([email protected]) focuses on approaches for supporting effective and efficient human decision making in different types of recommendation scenarios.

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.

Recommenders systems are used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based ones recommend items similar to those a given user has liked in the past. Indeed, the past behavior is supposed to be a reliable indicator of her future behavior. This assumption, however, causes the overspecialization problem. Our purpose is to mitigate the problem stimulating users and facilitating the serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and a serendipitous heuristic in order to provide also surprising suggestions. The reference scenario concerns with personalized tours in a museum and serendipitous items are introduced by slight diversions on the context-aware tours.

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.

Content-based recommender systems try to recommend items similar to those a given user has liked in the past. The basic process consists of matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object (item).

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.

Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/ searched/ visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions.

Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r)evolution and the advent of user generated content have changed the game for personalization, since the role of people has evolved from passive consumers of information to that of active contributors. One of the forms of user generated content that has drawn more attention from the research community is folksonomy, a taxonomy generated by users who collaboratively annotate and categorize resources of interests with freely chosen keywords called tags.

An electronic performance support system (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project aims at integrating an EPSS with a hybrid recommender system. Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. The main contribution of this paper is a content- collaborative hybrid recommender which computes similarities between users relying on their content- based profiles, in which user preferences are stored, instead of comparing their rating styles. A distinctive feature of our system is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in WordNet. This model, named "semantic user profile", is exploited by the hybrid recommender in the neighborhood formation process.

Recommender systems help to reduce information overload and provide customized information access for targeted domains. Such systems take input from users and, based on their needs and preferences, provide personalized advices that help people to filter useful information. Collaborative filtering and content-based filtering are the most widely recommendation techniques adopted to date. The paper presents a new hybrid recommendation technique based on the combination of classic collaborative filtering and user profiles inferred using content-based methods.