We examine the cold-start recommendation task in an online retail setting for users who have not yet purchased (or interacted in a meaningful way with) any available items but who have granted access to limited side information, such as basic demographic data (gender, age, location) or social network information (Facebook friends or page likes). We formalize neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available. In real-data experiments with 30,000 users who purchased 80,000+ books and had 9,000,000+ Facebook friends and 6,000,000+ page likes, we show that using Facebook page likes for cold-start recommendation yields up to a 3-fold improvement in mean average precision (mAP) and up to 6-fold improvements in Precision@k and Recall@k compared to most-popular-item, demographic, and Facebook friend cold-start recommenders. These results demonstrate the substantial predictive power of social network content, and its significant utility in a challenging problem -recommendation for cold-start users.
The effective tailoring of decisions to the needs and desires of specific users requires automated mechanisms for preference assessment. We provide a brief overview of recent direct preference elicitation methods: these methods ask users to answer (ideally, a small number of) queries regarding their preferences and use this information to recommend a feasible decision that would be (approximately) optimal given those preferences. We argue for the importance of assessing numerical utilities rather than qualitative preferences, and survey several utility elicitation techniques from artificial intelligence, operations research, and conjoint analysis.
Product recommendation and decision support systems must generally develop a model of user preferences by querying or otherwise interacting with a user. Recent approaches to elicitation using minimax regret have proven to be very powerful in simulation. In this work, we test both the effectiveness of regret-based elicitation, and user comprehension and acceptance of minimax regret in user studies. We report on a study involving 40 users interacting with the UTPREF Recommendation System, which helps students navigate and find rental accommodation. UTPREF maintains an explicit (but incomplete) generalized additive utility (GAI) model of user preferences, and uses minimax regret for recommendation. We assess the following general questions: How effective is regret-based elicitation in finding optimal or near-optimal products? Do users understand and accept the minimax regret criterion in practice? Do decision-theoretically valid queries for GAI models result in more accurate assessment than simpler, ad hoc queries? On the first two issues, we find that the minimax regret decision criterion is effective, understandable, and intuitively appealing. On the third issue, we find that simple, semantically ambiguous query types perform as well as more demanding, semantically valid queries for GAI models. We also assess the relative difficulty of specific query types.
In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks, we argue that it is hindered by two limitations: first, it does not produce user-personalised predictions, which hampers recommendation performance; second, it involves solving a constrained optimisation problem, which impedes fast training. In this paper, we propose LRec, a variant of SLIM that overcomes these limitations without sacrificing any of SLIM's strengths.At its core, LRec employs linear logistic regression; despite this simplicity, LRec consistently and significantly outperforms all existing methods on a range of datasets. Our results thus illustrate that the OC-CF problem can be effectively tackled via linear classification models.
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