DeepRec: A deep neural network approach to recommendation with item embedding and weighted loss function

Abstract Traditional collaborative filtering techniques suffer from the data sparsity problem in practice. That is, only a small proportion of all items in the recommender system occur in a user's rated item list. However, in order to retrieve items meeting a user's interest, all possible candidate items should be investigated. To address this problem, this paper proposes a recommendation approach called DeepRec, based on feedforward deep neural network learning with item embedding and weighted loss function. Specifically, item embedding learns numerical vectors for item representation, and weighted loss function balances popularity and novelty of recommended items. Moreover, it introduces two strategies, i.e. sampling by random (Ran-Strategy) and sampling by distribution (Pro-Strategy), to leave one item as output and the remaining as input from each user's historically rated item list. Max-pooling and average-pooling are employed to combine individual item vectors to derive users’ input vectors for feedforward deep neural network learning. Experiments on the App dataset and the Last.fm dataset demonstrate that the proposed DeepRec approach is superior to state-of-the-art techniques in recommending Apps and songs in terms of accuracy and diversity as well as complexity.