A hybrid recommender system for improving automatic playlist continuation

Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.This work has been partially supported by the Catalan Agency for Management of University and Research Grants (AGAUR) (2017 SGR 574), by the European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program –COMPETE 2020– (POCI-01-0145-FEDER006961), and by the Portuguese Foundation for Science and Technology (FCT) (UID/EEA/50014/2013).Peer ReviewedPostprint (author's final draft

A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation

International audienceHow can we effectively recommend items to a user about whom we have no information? This is the problem we focus on, known as the cold-start problem. In this paper, we focus on the cold user problem.In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information?Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS.In comparison with two baselines, PdMS improves the performance as measured by the nDCG.These improvements are demonstrated on real, public datasets

Mining HCI Data for Theory of Mind Induction

Human-computer interaction (HCI) results in enormous amounts of data-bearing potentials for understanding a human user’s intentions, goals, and desires. Knowing what users want and need is a key to intelligent system assistance. The theory of mind concept known from studies in animal behavior is adopted and adapted for expressive user modeling. Theories of mind are hypothetical user models representing, to some extent, a human user’s thoughts. A theory of mind may even reveal tacit knowledge. In this way, user modeling becomes knowledge discovery going beyond the human’s knowledge and covering domain-specific insights. Theories of mind are induced by mining HCI data. Data mining turns out to be inductive modeling. Intelligent assistant systems inductively modeling a human user’s intentions, goals, and the like, as well as domain knowledge are, by nature, learning systems. To cope with the risk of getting it wrong, learning systems are equipped with the skill of reflection

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

When personalization is not an option: An in-the-wild study on persuasive news recommendation

Aiming at granting wide access to their contents, online information providers often choose not to have registered users, and therefore must give up personalization. In this paper, we focus on the case of non-personalized news recommender systems, and explore persuasive techniques that can, nonetheless, be used to enhance recommendation presentation, with the aim of capturing the user’s interest on suggested items leveraging the way news is perceived. We present the results of two evaluations “in the wild”, carried out in the context of a real online magazine and based on data from 16,134 and 20,933 user sessions, respectively, where we empirically assessed the effectiveness of persuasion strategies which exploit logical fallacies and other techniques. Logical fallacies are inferential schemes known since antiquity that, even if formally invalid, appear as plausible and are therefore psychologically persuasive. In particular, our evaluations allowed us to compare three persuasive scenarios based on the Argumentum Ad Populum fallacy, on a modified version of the Argumentum ad Populum fallacy (Group-Ad Populum), and on no fallacy (neutral condition), respectively. Moreover, we studied the effects of the Accent Fallacy (in its visual variant), and of positive vs. negative Framing

How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation

Recommender Systems have shown to be an efective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. ELLIOT is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple confguration fle. The framework loads, flters, and splits the data considering a vast set of strategies. Then, it optimizes hyperparameters for several recommendation algorithms, selects the best models, compares them with the baselines, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis. The aim is to provide researchers a tool to ease all the experimental evaluation phases (and make them reproducible), from data reading to results collection. ELLIOT is freely available on GitHub at https://github.com/sisinflab/ellio

Content-Based Multimedia Recommendation Systems: Definition and Application Domains

The goal of this work is to formally provide a general definition of a multimedia recommendation system (MMRS), in particular a content-based MMRS (CB-MMRS), and to shed light on different applications of multimedia content for solving a variety of tasks related to recommendation. We would like to disambiguate the fact that multimedia recommendation is not only about recommending a particular media type (e.g., music, video), rather there exists a variety of other applications in which the analysis of multimedia input can be usefully exploited to provide recommendations of various kinds of information

Using implicit feedback for recommender systems: characteristics, applications, and challenges

Recommender systems are software tools to tackle the problem of information overload by helping users to find items that are most relevant for them within an often unmanageable set of choices. To create these personalized recommendations for a user, the algorithmic task of a recommender system is usually to quantify the user's interest in each item by predicting a relevance score, e.g., from the user's current situation or personal preferences in the past. Nowadays, recommender systems are used in various domains to recommend items such as products on e-commerce sites, movies and music on media portals, or people in social networks. To assess the user's preferences, recommender systems proposed in past research often utilized explicit feedback, i.e., deliberately given ratings or like/dislike statements for items. In practice, however, in many of today's application domains of recommender systems this kind of information is not existent. Therefore, recommender systems have to rely on implicit feedback that is derived from the users' behavior and interactions with the system. This information can be extracted from navigation or transaction logs. Using implicit feedback leads to new challenges and open questions regarding, for example, the huge amount of signals to process, the ambiguity of the feedback, and the inevitable noise in the data. This thesis by publication explores some of these challenges and questions that have not been covered in previous research. The thesis is divided into two parts. In the first part, the thesis reviews existing works on implicit feedback and recommender systems that exploit these signals, especially in the Social Information Access domain, which utilizes the "community wisdom" of the social web for recommendations. Common application scenarios for implicit feedback are discussed and a categorization scheme that classifies different types of observable user behavior is established. In addition, state-of-the-art algorithmic approaches for implicit feedback are examined that, e.g., interpret implicit signals directly or convert them to explicit ratings to be able to use "classic" recommendation approaches that were designed for explicit feedback. The second part of the thesis comprises some of the author's publications that deal with selected challenges of implicit feedback based recommendations. These contain (i) a specialized learning-to-rank algorithm that can differentiate different levels of interest indicator strength in implicit signals, (ii) contextualized recommendation techniques for the e-commerce domain that adapt product suggestions to customers' current short-term goals as well as their long-term preferences, and (iii) intelligent reminding approaches that aim at the re-discovery of relevant items in a customer's browsing history. Furthermore, the last paper of the thesis provides an in-depth analysis of different biases of various recommendation algorithms. Especially the popularity bias, the tendency to recommend mostly popular items, can be problematic in practical settings and countermeasures to reduce this bias are proposed