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Learning Distributed Representations for Recommender Systems with a Network Embedding Approach

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Information Retrieval Technology (AIRS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9994))

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In this paper, we present a novel perspective to address recommendation tasks by utilizing the network representation learning techniques. Our idea is based on the observation that the input of typical recommendation tasks can be formulated as graphs. Thus, we propose to use the k-partite adoption graph to characterize various kinds of information in recommendation tasks. Once the historical adoption records have been transformed into a graph, we can apply the network embedding approach to learn vertex embeddings on the k-partite adoption network. Embeddings for different kinds of information are projected into the same latent space, where we can easily measure the relatedness between multiple vertices on the graph using some similarity measurements. In this way, the recommendation task has been casted into a similarity evaluation process using embedding vectors. The proposed approach is both general and scalable. To evaluate the effectiveness of the proposed approach, we construct extensive experiments on two different recommendation tasks using real-world datasets. The experimental results have shown the superiority of our approach. To the best of our knowledge, it is the first time that a network representation learning approach has been applied to recommendation tasks.

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  1. 1.

    A tag itself can be treated as an item, too. Here we follow the conventions in tag recommendation which distinguishes between an item and a tag.

  2. 2.

  3. 3.

    The dataset was originally used for rating prediction, and we use it for item recommendation.

  4. 4.

    The number of items in both datasets is large, and it will be quite time-consuming to consider all the unadopted items as candidate recommendations. We follow [24] to pair each adopted item with 50 negative unadopted items to form the candidate recommendation list.

  5. 5.

    We do not compare with other methods with item contents or temporal information.


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The authors thank the anonymous reviewers for their valuable and constructive comments. The work was partially supported by National Natural Science Foundation of China under the grant number 61502502 and Beijing Natural Science Foundation under the grant number 4162032.

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Correspondence to Wayne Xin Zhao .

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Zhao, W.X., Huang, J., Wen, JR. (2016). Learning Distributed Representations for Recommender Systems with a Network Embedding Approach. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham.

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