2017 TowardBetterInteractionsinRecom

(Zhao et al., 2017) ⇒ Qian Zhao, Gediminas Adomavicius, F. Maxwell Harper, Martijn Willemsen, and Joseph A. Konstan. (2017). “Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists.” In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ISBN:978-1-4503-4335-0 doi:10.1145/2998181.2998211

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Keywords

recommender systems; user study; field experiment

Abstract

Current recommender systems often show the same most-highly 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.

1. INTRODUCTION

Recommender systems typically are optimized to produce a top-N list reflective of the most-highly recommended items a user has not yet rated. However, there are many reasons to believe that this order may not be the best order to present items to users, either within or across sessions. First, top-N does not consider whether a recommendation has already been displayed to the user before, that is, whether it is fresh vs. potentially stale. Second, presenting the standard top-N list may create an experience where continued exploration results in a sense of finding ever-worse alternatives recommended. In this paper, we explore two alternatives to the standard top-N approach designed to address these concerns. Cycling recommendations demotes recommended items after they have been viewed several times, while promoting fresher recommendations from the lower portions of the list. Serpentining displays a "zig-zag" order, in which the best recommendations (i.e., the top recommendations from a rating prediction model) are spread across several pages, offering high-quality items on each page as a user continues to explore. Cycling may happen within the same visit or across multiple visits, which we call intra-session or inter-session cycling. Intra-session cycling creates a more immediate and noticeable change but may cause confusion because potentially interesting recommendations may disappear when a user goes back to the previous page. Inter-session cycling is less likely to have this problem but may not be noticeable because users have forgotten what they saw previously.

The high-level research question in this work is whether cycling and serpentining – as two perspectives of re-examining top-N list – improve user experience. However, we are not trying to optimize a particular user experience. We recognize that different experiences may require different approaches. A situation where a site recommends a single item cannot benefit from serpentining. A user who treats the top-N list as a "to-do" list, taking the top item each time, would not be served well by cycling. Rather, we want to see how these manipulations relate to user experience in the hopes of guiding designers in adopting them, or offering them to users. Similarly to the finding from Ziegler’s work [32] that users are willing to accept a certain loss of accuracy in order to have more diverse recommendations, we expect that the perceived accuracy of recommendations may get reduced because of the manipulation; however, we test whether the accuracy reduction may be preferred in exchange for the exposure to a broader and “fresher” set of items.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 TowardBetterInteractionsinRecom	Joseph A. Konstan Qian Zhao Gediminas Adomavicius F. Maxwell Harper Martijn Willemsen			Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists				10.1145/2998181.2998211		2017