Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendationKnowledge-Based Systems

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Authors
Weike Pan, Zhuode Liu, Zhong Ming, Hao Zhong, Xin Wang, Congfu Xu
Year
2015
DOI
10.1016/j.knosys.2015.05.009
Subject
Software / Information Systems and Management / Management Information Systems / Artificial Intelligence

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Text

ri n g b na

Article history:

Received 11 August 2014

Received in revised form 10 February 2015

Accepted 8 May 2015

Available online 15 May 2015

Keywords:

Collaborative recommendation

Heterogeneous feedbacks

Collaborative recommendation has attracted various research works in recent years. However, an impor[28,31] and implicit feedbacks in Bayesian personalized ranking (BPR) [27]. However, few works have studied a very common problem setting, in which ‘‘a user examined several items but only rated a few’’. This setting is called heterogeneous collaborative recommendation (HCR) and considers different types of users’ feedbacks, ypes of feedbacks le defined on one o that defi . Because i feedbacks are usually much more than explicit feedbacks, le ing raw implicit feedbacks will increase the time cost and cost significantly, which may make it not applicable in real-world recommendation scenarios. The increase of time and space cost is also observed in our empirical studies in Section 4.

In order to leverage the implicit feedbacks in a more efficient and effective way, we address the HCR problem from a novel transfer learning perspective [19], in which we take explicit feedbacks as target data and implicit feedbacks as auxiliary data.

Technically, we propose a novel two-step transfer learning ⇑ Corresponding author.

E-mail addresses: panweike@szu.edu.cn (W. Pan), zero.lzd@gmail.com (Z. Liu), mingz@szu.edu.cn (Z. Ming), haozhong@zju.edu.cn (H. Zhong), cswangxinm@zju. edu.cn (X. Wang), xucongfu@zju.edu.cn (C. Xu).

Knowledge-Based Systems 85 (2015) 234–244

Contents lists availab

Knowledge-Ba .e lrithms with low-rank assumptions have dominated in various recommendation scenarios due to their applicability and high accuracy. Most factorization based methods focus on homogeneous user feedbacks, e.g., explicit ratings in matrix factorization machine (FM) [24]. SVD++ and FM combine two t in a principled way via changing the prediction ru (user, item, rating) triple in explicit feedbacks t both the triple and all examined items by the userhttp://dx.doi.org/10.1016/j.knosys.2015.05.009 0950-7051/ 2015 Elsevier B.V. All rights reserved.ned on mplicit veragspace some1. Introduction

Recommendation functionality has been widely implemented as a default module in various Internet services such as

YouTube’s video recommendation and Amazon’s book recommendation. Factorization based collaborative recommendation algoincluding implicit examinations (e.g., browsing and clicks) and explicit ratings. In a typical recommendation system, implicit feedbacks are usually more abundant and thus have a potential to help alleviate the sparsity problem of users’ explicit ratings.

For the HCR problem, the most well-known method is probably the SVD++ model [11], which could be mimicked by factorizationFactorization machine

Compressed knowledge

Transfer learningtant problem setting, i.e., ‘‘a user examined several items but only rated a few’’, has not received much attention yet. We coin this problem heterogeneous collaborative recommendation (HCR) from the perspective of users’ heterogeneous feedbacks of implicit examinations and explicit ratings. In order to fully exploit such different types of feedbacks, we propose a novel and generic solution called compressed knowledge transfer via factorization machine (CKT-FM). Specifically, we assume that the compressed knowledge of user homophily and item correlation, i.e., user groups and item sets behind two types of feedbacks, are similar and then design a two-step transfer learning solution including compressed knowledge mining and integration. Our solution is able to transfer high quality knowledge via noise reduction, to model rich pairwise interactions among individual-level and cluster-level entities, and to adapt the potential inconsistent knowledge from implicit feedbacks to explicit feedbacks. Furthermore, the analysis on time complexity and space complexity shows that our solution is much more efficient than the state-of-the-art method for heterogeneous feedbacks. Extensive empirical studies on two large data sets show that our solution is significantly better than the state-of-the-art non-transfer learning method w.r.t. recommendation accuracy, and is much more efficient than that of leveraging the raw implicit examinations directly instead of compressed knowledge w.r.t. CPU time and memory usage. Hence, our CKT-FM strikes a good balance between effectiveness and efficiency of knowledge transfer in HCR.  2015 Elsevier B.V. All rights reserved.a r t i c l e i n f o a b s t r a c tCompressed knowledge transfer via facto for heterogeneous collaborative recomme

Weike Pan a, Zhuode Liu a, Zhong Ming a,⇑, Hao Zhon aCollege of Computer Science and Software Engineering, Shenzhen University, China b Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Chi journal homepage: wwwzation machine dation , Xin Wang b, Congfu Xu b le at ScienceDirect sed Systems sevier .com/locate /knosys solution, i.e., compressed knowledge transfer via factorization machine (CKT-FM), for knowledge sharing between auxiliary data and target data. In our first step, we mine compressed knowledge of user homophily (i.e., user groups) and item correlation (i.e., item sets) from auxiliary implicit feedbacks, which is expected to be more parsimonious than the raw implicit feedbacks. In our second step, we design an integrative knowledge transfer solution via expanding the design matrix of factorization machine, which incorporates the compressed knowledge of user groups and item sets into the target data in a seamless manner. We then conduct extensive empirical studies on two large data sets and obtain significantly better results via our CKT-FM than the state-of-the-art and auxiliary data, which can thus be categorized as a frontal-side transfer learning setting [23], rather than the two-side [22], user-side [7] or item-side [29] knowledge transfer setting. We illustrate our studied problem in Fig. 1, in particular of the left part (implicit examinations) and the right part (explicit ratings).