Intent-oriented diversity in recommender systems

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, http://dx.doi.org/10.1145/2009916.2010124.Diversity as a relevant dimension of retrieval quality is receiving increasing attention in the Information Retrieval and Recommender Systems (RS) fields. The problem has nonetheless been approached under different views and formulations in IR and RS respectively, giving rise to different models, methodologies, and metrics, with little convergence between both fields. In this poster we explore the adaptation of diversity metrics, techniques, and principles from ad-hoc IR to the recommendation task, by introducing the notion of user profile aspect as an analogue of query intent. As a particular approach, user aspects are automatically extracted from latent item features. Empirical results support the proposed approach and provide further insights.This work is supported by the Spanish Government (TIN2008- 06566-C04-02), and the Government of Madrid (S2009TIC-1542)

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

Goal-based structuring in a recommender systems

Recommender systems help people to find information that is interesting to them. However, current recommendation techniques only address the user's short-term and long-term interests, not their immediate interests. This paper describes a method to structure information (with or without using recommendations) taking into account the users' immediate interests: a goal-based structuring method. Goal-based structuring is based on the fact that people experience certain gratifications from using information, which should match with their goals. An experiment using an electronic TV guide shows that structuring information using a goal-based structure makes it easier for users to find interesting information, especially if the goals are used explicitly; this is independent of whether recommendations are used or not. It also shows that goal-based structuring has more influence on how easy it is for users to find interesting information than recommendations

“Get lost, GetLostBot!” annoying people by offering recommendations when they are not wanted

This brief paper outlines the experience of releasing a purposefully contrary recommendation service for Foursquare called GetLostBot. GetLostBot works differently than most recommender systems since it is responsive to behaviours rather than user requests. The system automatically monitors Foursquare behaviour and intervenes with suggestions when users fall into a routine. These interventions take the form of mysterious walking directions that challenge the user to visit somewhere new. Importantly, these suggestions are explicitly not informed by traditional metrics such as popularity, high ratings, or friend activity, and instead act as prompts to explore unknown places. This paper discusses the reception to the application, highlighting the apparent disconnect between users’ good intentions around becoming more serendipitous, and the reality of those interventions as they are experienced in the wild

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Explicit relevance models in intent-oriented information retrieval diversification

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, http://dx.doi.org/10.1145/2348283.2348297.The intent-oriented search diversification methods developed in the field so far tend to build on generative views of the retrieval system to be diversified. Core algorithm components in particular redundancy assessment are expressed in terms of the probability to observe documents, rather than the probability that the documents be relevant. This has been sometimes described as a view considering the selection of a single document in the underlying task model. In this paper we propose an alternative formulation of aspect-based diversification algorithms which explicitly includes a formal relevance model. We develop means for the effective computation of the new formulation, and we test the resulting algorithm empirically. We report experiments on search and recommendation tasks showing competitive or better performance than the original diversification algorithms. The relevance-based formulation has further interesting properties, such as unifying two well-known state of the art algorithms into a single version. The relevance-based approach opens alternative possibilities for further formal connections and developments as natural extensions of the framework. We illustrate this by modeling tolerance to redundancy as an explicit configurable parameter, which can be set to better suit the characteristics of the IR task, or the evaluation metrics, as we illustrate empirically.This work was supported by the national Spanish projects TIN2011-28538-C02-01 and S2009TIC-1542

A comparison of calibrated and intent-aware recommendations

Calibrated and intent-aware recommendation are recent approaches to recommendation that have apparent similarities. Both try, to a certain extent, to cover the user's interests, as revealed by her user profile. In this paper, we compare them in detail. On two datasets, we show the extent to which intent-aware recommendations are calibrated and the extent to which calibrated recommendations are diverse. We consider two ways of defining a user's interests, one based on item features, the other based on subprofiles of the user's profile. We find that defining interests in terms of subprofiles results in highest precision and the best relevance/diversity trade-off. Along the way, we define a new version of calibrated recommendation and three new evaluation metrics

Evaluating Conversational Recommender Systems: A Landscape of Research

Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances in voice-controlled devices, natural language processing, and AI in general, such systems received increased attention in recent years. Technically, conversational recommenders are usually complex multi-component applications and often consist of multiple machine learning models and a natural language user interface. Evaluating such a complex system in a holistic way can therefore be challenging, as it requires (i) the assessment of the quality of the different learning components, and (ii) the quality perception of the system as a whole by users. Thus, a mixed methods approach is often required, which may combine objective (computational) and subjective (perception-oriented) evaluation techniques. In this paper, we review common evaluation approaches for conversational recommender systems, identify possible limitations, and outline future directions towards more holistic evaluation practices