Minimal Interaction Content Discovery in Recommender Systems
Minimal Interaction Content Discovery in Recommender Systems
复制标题
推荐系统中的最小交互内容发现
DOI：
10.1145/2845090
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
S. Berkovsky
中科院分区：
文献类型：
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作者：
B. Kveton;S. Berkovsky
在推荐系统中，许多以前的工作都集中在提高项目评分预测的准确性上。相比之下，推荐界面和用户-推荐者互动领域仍未得到充分探索。在这项工作中，我们研究了用户与推荐列表的交互，旨在设计一种简化内容发现并将访问感兴趣项目的成本降至最低的方法。我们通过用户与推荐列表的交互次数(点击和滚动)来量化这一成本。为此，我们提出了广义线性搜索(GLS)，这是已有的线性搜索和广义搜索(GS)方法的自适应组合。GLS利用了这两种方法的优势，我们从形式上证明了它的性能至少与GS一样好。我们还对GLS进行了彻底的实验评估，并在离线和现场评估中将其与几种基线和启发式方法进行了比较。评估结果表明，GLS的性能始终优于基线方法，也受到用户的青睐。总之，GLS为推荐系统中的内容发现提供了一种高效且易于使用的方法。
Many prior works in recommender systems focus on improving the accuracy of item rating predictions. In comparison, the areas of recommendation interfaces and user-recommender interaction remain underexplored. In this work, we look into the interaction of users with the recommendation list, aiming to devise a method that simplifies content discovery and minimizes the cost of reaching an item of interest. We quantify this cost by the number of user interactions (clicks and scrolls) with the recommendation list. To this end, we propose generalized linear search (GLS), an adaptive combination of the established linear and generalized search (GS) approaches. GLS leverages the advantages of these two approaches, and we prove formally that it performs at least as well as GS. We also conduct a thorough experimental evaluation of GLS and compare it to several baselines and heuristic approaches in both an offline and live evaluation. The results of the evaluation show that GLS consistently outperforms the baseline approaches and is also preferred by users. In summary, GLS offers an efficient and easy-to-use means for content discovery in recommender systems.