When the System Becomes Your Personal Docent: Curated Book Recommendations

Curation is the act of selecting, organizing, and presenting content most often guided by professional or expert knowledge. While many popular applications have attempted to emulate this process by turning users into curators, we put an accent on a recommendation system which can leverage multiple data sources to accomplish the curation task. We introduce QBook, a recommender that acts as a personal docent by identifying and suggesting books tailored to the various preferences of each individual user. The goal of the designed system is to address several limitations often associated with recommenders in order to provide diverse and personalized book recommendations that can foster trust, effectiveness of the system, and improve the decision making process. QBook considers multiple perspectives, from analyzing user reviews, user historical data, and items\u27 metadata, to considering experts\u27 reviews and constantly evolving users\u27 preferences, to enhance the recommendation process, as well as quality and usability of the suggestions. QBook pairs each generated suggestion with an explanation that (i) showcases why a particular book was recommended and (ii) helps users decide which items, among the ones recommended, will best suit their individual interests. Empirical studies conducted using the Amazon/LibraryThing benchmark corpus demonstrate the correctness of the proposed methodology and QBook\u27s ability to outperform baseline and state-of-the-art methodologies for book recommendations

Chain-based recommendations

Recommender systems are discovery tools. Typically, they infer a user's preferences from her behaviour and make personalized suggestions. They are one response to the overwhelming choices that the Web affords its users. Recent studies have shown that a user of a recommender system is more likely to be satisfied by the recommendations if the system provides explanations that allow the user to understand their rationale, and if the system allows the user to provide feedback on the recommendations to improve the next round of recommendations so that they take account of the user's ephemeral needs. The goal of this dissertation is to introduce a new recommendation framework that offers a better user experience, while giving quality recommendations. It works on content-based principles and addresses both the issues identified in the previous paragraph, i.e.\ explanations and recommendation feedback. We instantiate our framework to produce two recommendation engines, each focusing on one of the themes: (i) the role of explanations in producing recommendations, and (ii) helping users to articulate their ephemeral needs. For the first theme, we show how to unify recommendation and explanation to a greater degree than has been achieved hitherto. This results in an approach that enables the system to find relevant recommendations with explanations that have a high degree of both fidelity and interpretability. For the second theme, we show how to allow users to steer the recommendation process using a conversational recommender system. Our approach allows the user to reveal her short-term preferences and have them taken into account by the system and thus assists her in making a good decision efficiently. Early work on conversational recommender systems considers the case where the candidate items have structured descriptions (e.g.\ sets of attribute-value pairs). Our new approach works in the case where items have unstructured descriptions (e.g.\ sets of genres or tags). For each of the two themes, we describe the problem settings, the state-of-the-art, our system design and our experiment design. We evaluate each system using both offline analyses as well as user trials in a movie recommendation domain. We find that the proposed systems provide relevant recommendations that also have a high degree of serendipity, low popularity-bias and high diversity

Learning Explainable User Sentiment and Preferences for Information Filtering

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often result from interactions such as sharing or liking, but also from interactions in natural language. The continuous growth of the amount of content and of digital traces has led to information overload: surrounded by large volumes of information, people are facing difficulties when searching for information relevant to their interests. To improve user experience, information systems must be able to assist users in achieving their search goals, effectively and efficiently. This thesis is concerned with two important challenges that information systems need to address in order to significantly improve search experience and overcome information overload. First, these systems need to model accurately the variety of user traces, and second, they need to meaningfully explain search results and recommendations to users. To address these challenges, this thesis proposes novel methods based on machine learning to model user sentiment and preferences for information filtering systems, which are effective, scalable, and easily interpretable by humans. We focus on two prominent types of user traces in social networks: on the one hand, user comments accompanied by unary preferences such as likes, and on the other hand, user reviews accompanied by numerical preferences such as star ratings. In both cases, we advocate that by better understanding user text through mining its semantics and modeling its structure, we can not only improve information filtering, but also explain predictions to users. Within this context, we aim to answer three main research questions, namely: (i)~how do item semantics help to predict unary preferences; (ii)~how do sentiments of free-form user texts help to predict unary preferences; and (iii)~how to model fine-grained numerical preferences from user review texts. Our goal is to model and extract from user text the knowledge required to answer these questions, and to obtain insights on how to design better information filtering systems that are more effective and improve user experience. To answer the first question, we formulate the recommendation problem based on unary preferences as a top-N retrieval task and we define an appropriate dataset and metrics for measuring performance. Then, we propose and evaluate several content-based methods based on semantic similarities under presence or absence of preferences. To answer the second question, we propose a sentiment-aware neighborhood model which integrates the sentiment of user comments with unary preferences, either through fixed or through learned mapping functions. For the latter type, we propose a learning algorithm which adapts the sentiment of user comments to unary preferences at collective or individual levels. To answer the third question, we cast the problem of modeling user attitude toward aspects of items as a weakly supervised problem, and we propose a weighted multiple-instance learning method for solving it. Lastly, we show that the learned saliency weights, apart from being easily interpretable, are useful indicators for review segmentation and summarization