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

TL;DR

This paper demonstrates that many recommendation problems can be effectively reformulated as ad-hoc retrieval tasks, combining retrieval models, collaborative filtering, and neural embeddings for improved music recommendations.

Contribution

It introduces a novel approach that unifies recommendation and ad-hoc retrieval methods, achieving competitive results in a music recommendation challenge.

Findings

Achieved rank 7 out of 112 in RecSys 2018 challenge

Effective use of pseudo-relevance feedback as collaborative filtering

Combined retrieval, content-based, and neural embedding methods

Abstract

The popular approaches to recommendation and ad-hoc retrieval tasks are largely distinct in the literature. In this work, we argue that many recommendation problems can also be cast as ad-hoc retrieval tasks. To demonstrate this, we build a solution for the RecSys 2018 Spotify challenge by combining standard ad-hoc retrieval models and using popular retrieval tools sets. We draw a parallel between the playlist continuation task and the task of finding good expansion terms for queries in ad-hoc retrieval, and show that standard pseudo-relevance feedback can be effective as a collaborative filtering approach. We also use ad-hoc retrieval for content-based recommendation by treating the input playlist title as a query and associating all candidate tracks with meta-descriptions extracted from the background data. The recommendations from these two approaches are further supplemented by a nearest neighbor search based on track embeddings learned by a popular neural model. Our final ranked list of recommendations is produced by a learning to rank model. Our proposed solution using ad-hoc retrieval models achieved a competitive performance on the music recommendation task at RecSys 2018 challenge---finishing at rank 7 out of 112 participating teams and at rank 5 out of 31 teams for the main and the creative tracks, respectively.