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The popular approaches to recommendation and ad-hoc retrieval tasks arelargely distinct in the literature. In this work, we argue that manyrecommendation problems can also be cast as ad-hoc retrieval tasks. Todemonstrate this, we build a solution for the RecSys 2018 Spotify challenge bycombining standard ad-hoc retrieval models and using popular retrieval toolssets. We draw a parallel between the playlist continuation task and the task offinding good expansion terms for queries in ad-hoc retrieval, and show thatstandard pseudo-relevance feedback can be effective as a collaborativefiltering approach. We also use ad-hoc retrieval for content-basedrecommendation by treating the input playlist title as a query and associatingall candidate tracks with meta-descriptions extracted from the background data.The recommendations from these two approaches are further supplemented by anearest neighbor search based on track embeddings learned by a popular neuralmodel. Our final ranked list of recommendations is produced by a learning torank model. Our proposed solution using ad-hoc retrieval models achieved acompetitive performance on the music recommendation task at RecSys 2018challenge---finishing at rank 7 out of 112 participating teams and at rank 5out of 31 teams for the main and the creative tracks, respectively.

A Line in the Sand: Recommendation or Ad-hoc Retrieval?