Toward Better Interactions in Recommender Systems

Abstract

Current recommender systems often show the same mosthighly recommended items again and again ignoring the feedback that users neither rate nor click on those items. We conduct an online field experiment to test two ways of manipulating top-N recommendations with the goal of improving user experience: cycling the top-N recommendation based on their past presentation and serpentining the top-N list mixing the best items into later recommendation requests. We find interesting tensions between opt-outs and activities, user perceived accuracy and freshness. Cycling within the same session might be a "love it or hate it" recommender property because users in it have a higher opt-out rate but engage in more activities. Cycling across sessions and serpentining increase user activities without significantly affecting opt-out rates. Users perceive more change and freshness but less accuracy and familiarity. Combining cycling and serpentining does not work as well as each individual manipulation separately. These two ways of manipulations on top-N list demonstrate some attractive properties but also call for innovative approaches to overcome their potential costs.

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“…In fact, when we mixed them in top news, the overall recognition rate would be the highest -11% significantly higher than the original top news and 19% higher than the random top news. This is, to some extent, similar to Zhao et al's finding that to keep users browsing a recommender website, we can not put all the interesting items at the top [58]. Instead, we need to distribute it across the page and mix them with other items we would like to expose to users.…”

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“…Some works have already tried to use impressions to build better recommendation models in various ways: [4,5,16,33,38,39] use impression data to compute features, re-ranking, sampling and to learn biases. Furthermore [6,10,14,17,18,31,36] apply neural or deep-learning models including impressions.…”