Subjective Similarity: Personalizing Alternative Item Recommendations
作者： Tolga Könik , Rajyashree Mukherjee , Jayasimha Katukuri
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摘要: We present a new algorithm for recommending alternatives to given item in an e-commerce setting. Our is incremental improvement over earlier system, which recommends similar items by first assigning the input clusters and then selecting best quality within those clusters. The original does not consider recent context our improves system personalizing recommendations user intentions. measures intention using queries, are used determine level of abstraction similarity relative importance dimensions. show that engagement increases when recommended titles share more terms with most queries. Moreover, query coverage without sacrificing quality.
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