Songs can be well arranged by professional music curators to form a riveting playlist that creates engaging listening experiences. However, it is time-consuming for curators to timely rearrange these playlists for fitting trends in future. By exploiting the techniques of deep learning and reinforcement learning, in this paper, we consider music playlist generation as a language modeling problem and solve it by the proposed attention language model with policy gradient. We develop a systematic and interactive approach so that the resulting playlists can be tuned flexibly according to user preferences. Considering a playlist as a sequence of words, we first train our attention RNN language model on baseline recommended playlists. By optimizing suitable imposed reward functions, the model is thus refined for corresponding preferences. The experimental results demonstrate that our approach not only generates coherent playlists automatically but is also able to flexibly recommend personalized playlists for diversity, novelty and freshness.
Beyond playlist generation, there are several works adopting the concept of playlist generation to facilitate recommendation systems. Given a set of songs, Vargas @math @cite_14 propose several scoring functions, such as diversity and novelty, and retrieve the top-K songs with higher scores for each user as the resulting recommended list of songs. Chen @math @cite_6 propose a query-based music recommendation system that allow users to select a preferred song as a seed song to obtain related songs as a recommended playlist.
@cite_14 @cite_6
148182375 2511954120
Beyond accuracy, novelty and diversity have attracted increasing interest as quality factors of Recommender Systems (RS) in the last few years. This paper presents work in progress towards the application of intentoriented IR diversity techniques to the RS field, and the formalization of novelty and diversity metrics for RS. Experimental results show that the resulting diversification techniques produce interesting results with respect to the metrics defined here. A common scenario considered in recommender systems is to predict a user's preferences on unseen items based on his her preferences on observed items. A major limitation of this scenario is that a user might be interested in different things each time when using the system, but there is no way to allow the user to actively alter or adjust the recommended results. To address this issue, we propose the idea of "query-based recommendation" that allows a user to specify his her search intention while exploring new items, thereby incorporating the concept of information retrieval into recommendation systems. Moreover, the idea is more desirable when the user intention can be expressed in different ways. Take music recommendation as an example: the proposed system allows a user to explore new song tracks by specifying either a track, an album, or an artist. To enable such heterogeneous queries in a recommender system, we present a novel technique called "Heterogeneous Preference Embedding" to encode user preference and query intention into low-dimensional vector spaces. Then, with simple search methods or similarity calculations, we can use the encoded representation of queries to generate recommendations. This method is fairly flexible and it is easy to add other types of information when available. Evaluations on three music listening datasets confirm the effectiveness of the proposed method over the state-of-the-art matrix factorization and network embedding methods.