Identifying Controversial Pairs in Item-to-Item Recommendations

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Suggestion techniques in large-scale on-line marketplaces are important to aiding customers in discovering new content material. Nevertheless, state-of-the-art techniques for item-to-item suggestion duties are sometimes based mostly on a shallow stage of contextual relevance, which may make the system inadequate for duties the place merchandise relationships are extra nuanced. Contextually related merchandise pairs can typically have problematic relationships which can be complicated and even controversial to finish customers, they usually might degrade consumer experiences and model notion when advisable to customers. For instance, the advice of a guide about one sports activities group to somebody studying a guide about that group’s largest rival may very well be a foul expertise, regardless of the presumed similarities of the books. On this paper, we suggest a classifier to establish and forestall such problematic item-to-item suggestions and to boost total consumer experiences. The proposed strategy makes use of energetic studying to pattern onerous examples successfully throughout delicate merchandise classes and employs human raters for knowledge labeling. We additionally carry out offline experiments to display the efficacy of this technique for figuring out and filtering problematic suggestions whereas sustaining suggestion high quality.