Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Modeling Users Feedback Using Bayesian Methods for Data-Driven Requirements Engineering

Data-driven requirements engineering represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. App developers now receive abundant user feedback from user comments in app stores and social media, i.e., explicit feedback, to feedback from usage data and system logs, i.e, implicit feedback. In this dissertation, we describe two novel Bayesian approaches that utilize the available user\u27s to support requirements decisions and activities in the context of applications delivered through software marketplaces (web and mobile). In the first part, we propose to exploit implicit user feedback in the form of usage data to support requirements prioritization and validation. We formulate the problem as a popularity prediction problem and present a novel Bayesian model that is highly interpretable and offers early-on insights that can be used to support requirements decisions. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and outperforms competitive models. In the second part, we discuss the limitations of previous approaches that use explicit user feedback for requirements extraction, and alternatively, propose a novel Bayesian approach that can address those limitations and offer a more efficient and maintainable framework. The proposed approach (1) simplifies the pipeline by accomplishing the classification and summarization tasks using a single model, (2) replaces manual steps in the pipeline with unsupervised alternatives that can accomplish the same task, and (3) offers an alternative way to extract requirements using example-based summaries that retains context. Experimental results demonstrate that the proposed approach achieves equal or better classification accuracy and outperforms competitive models in terms of summarization accuracy. Specifically, we show that the proposed approach can capture 91.3% of the discussed requirement with only 19% of the dataset, i.e., reducing the human effort needed to extract the requirements by 80%

Studies on Personalized HCI

Ph.DDOCTOR OF PHILOSOPH

ICML Exploration & Exploitation challenge: Keep it simple!

International audienceRecommendation has become a key feature in the economy of a lot of companies (online shopping, search engines...). There is a lot of work going on regarding recommender systems and there is still a lot to do to improve them. Indeed nowadays in many companies most of the job is done by hand. Moreover even when a supposedly smart recommender system is designed, it is hard to evaluate it without using real audience which obviously involves economic issues. The ICML Exploration & Exploitation challenge is an attempt to make people propose efficient recommendation techniques and particularly focuses on limited computational resources. The challenge also proposes a framework to address the problem of evaluating a recommendation algorithm with real data. We took part in this challenge and achieved the best performances; this paper aims at reporting on this achievement; we also discuss the evaluation process and propose a better one for future challenges of the same kind

Tag based Bayesian latent class models for movies : economic theory reaches out to big data science

For the past 50 years, cultural economics has developed as an independent research specialism. At its core are the creative industries and the peculiar economics associated with them, central to which is a tension that arises from the notion that creative goods need to be experienced before an assessment can be made about the utility they deliver to the consumer. In this they differ from the standard private good that forms the basis of demand theory in economic textbooks, in which utility is known ex ante. Furthermore, creative goods are typically complex in composition and subject to heterogeneous and shifting consumer preferences. In response to this, models of linear optimization, rational addiction and Bayesian learning have been applied to better understand consumer decision- making, belief formation and revision. While valuable, these approaches do not lend themselves to forming verifiable hypothesis for the critical reason that they by-pass an essential aspect of creative products: namely, that of novelty. In contrast, computer sciences, and more specifically recommender theory, embrace creative products as a study object. Being items of online transactions, users of creative products share opinions on a massive scale and in doing so generate a flow of data driven research. Not limited by the multiple assumptions made in economic theory, data analysts deal with this type of commodity in a less constrained way, incorporating the variety of item characteristics, as well as their co-use by agents. They apply statistical techniques supporting big data, such as clustering, latent class analysis or singular value decomposition. This thesis is drawn from both disciplines, comparing models, methods and data sets. Based upon movie consumption, the work contrasts bottom-up versus top-down approaches, individual versus collective data, distance measures versus the utility-based comparisons. Rooted in Bayesian latent class models, a synthesis is formed, supported by the random utility theory and recommender algorithm methods. The Bayesian approach makes explicit the experience good nature of creative goods by formulating the prior uncertainty of users towards both movie features and preferences. The latent class method, thus, infers the heterogeneous aspect of preferences, while its dynamic variant- the latent Markov model - gets around one of the main paradoxes in studying creative products: how to analyse taste dynamics when confronted with a good that is novel at each decision point. Generated by mainly movie-user-rating and movie-user-tag triplets, collected from the Movielens recommender system and made available as open data for research by the GroupLens research team, this study of preference patterns formation for creative goods is drawn from individual level data