Heterogeneous one-class collaborative filtering (HOCCF) is a recently studied important recommendation problem, which consists of different types of users' one-class feedback such as browses and purchases. In HOCCF, we aim to fully exploit the heterogenous feedback and learn users' preferences so as to make a personalized and ranking-oriented recommendation for each user. For HOCCF, we can apply existing solutions for OCCF with purchases only such as Bayesian personalized ranking (BPR) or make use of both browses and purchases such as transfer via joint similarity learning (TJSL). However, BPR may be not very accurate due to the ignorance of browses, and TJSL may be not very efficient due to the mechanism of joint similarity learning and base model aggregation. In this paper, we propose a novel perspective for the different types of one-class feedback via users' different roles, i.e., browser and purchaser. Specifically, we design a two-stage role-based preference learning framework, i.e., role-based Bayesian personalized ranking (RBPR). In RBPR, we first digest the combined one-class feedback as a browser to find the candidate items that a user will browse, and then we exploit the purchase feedback to refine the candidate list as a purchaser. Empirical results on five public datasets show that our RBPR is an efficient and accurate recommendation algorithm for HOCCF as compared with the state-of-the-art methods such as BPR and TJSL.
Intelligent recommendation systems and technology have
played a more and more important role in various real-world
applications, with a wide spectrum of entertainment, social
and professional services. Some recent work show that one
important line of research have gradually transferred from
collaborative filtering (CF) with numerical ratings to
oneclass CF (OCCF) with homogeneous one-class feedback such
as purchases [
The main challenge of HOCCF is the heterogeneity of the
two different types of one-class feedback, since a user’s
preference behind a purchase action may be different from that
of a browse action. In a very recent work [
In this paper, we interpret the HOCCF problem from a
novel view of users’ roles, i.e., a purchaser (as reflected in
a purchase feedback) is converted from a browser in a
sequential manner. Based on this perspective, we propose
a two-stage framework, including browser-based preference
learning and purchaser-based preference learning. Those
two preference learning tasks are connected via a candidate
list of items that a user will browse, which is assumed to
contain the potential items that a user will finally purchase.
In each of the two tasks, we apply the seminal work for
homogeneous one-class feedback, i.e., Bayesian personalized
ranking (BPR) [
In our empirical studies, we compare our RBPR with the state-of-the-art methods of BPR and TJSL using various ranking-oriented evaluation metrics on five public datasets. The studies show that our RBPR is able to produce competitive recommendations efficiently. We list our main contributions as follows: (i) we propose a novel and generic staged role-based preference learning framework, which is a frustratingly easy, scalable and effective solution for collaborative ranking with heterogeneous one-class feedback; and (ii) we conduct extensive empirical studies and obtain very promising results.
In HOCCF, we have a set of n users (U), a set of m items (I), and two different sets of user feedback, e.g., browses B and purchases P. Our goal is to find some likely-to-purchase items from unpurchased items for each user.
In order to fully exploit heterogeneous feedback in
HOCCF such as browses and purchases, we propose not to model
those different feedback jointly as a whole as done in a
recent work [
In the first step, we assume that a typical user is first a browser before he/she is converted to a purchaser. And thus, in our first task, we focus on answering the question of “whether a user will browse an item”.
In order to address this task, we propose to combine the
two types of one-class feedback, i.e., browses and
purchases, together, and then apply an algorithm for homogeneous
one-class feedback such as BPR [
Once we have learned the model parameters, we can generate a candidate list of items that a user is likely to browse. Specifically, for a top-K recommended problem, we will generate 3K items in this step, so that the refinement in next step may have more room for improvement. 2.3
In the second step, we assume that a user will most likely choose an item from the candidate list that he/she has browsed. For this reason, in our second task, we mainly answer the question of “whether a user will purchase an item”. Input: Users’ browses B and purchases P.
Output: Top-K recommended items for each user. Step 1. Conduct browser-based preference learning via BPR(B ∪ P) as shown in Eq.(1) and obtain 3K candidate items with highest predicted scores.
Step 2. Conduct purchaser-based preference learning via BPR(P) as shown in Eq.(2); predict the scores on the 3K candidate items and refine the list.
In order to solve this task, we propose to use the purchase data only to refine the candidate list from the first step. The reason is that the purchase feedback is more helpful in answering whether a certain item will be bought by a user. Due to the fact that a user’s purchase feedback are few, we may not get good results if we only apply the second step, i.e., only use the purchase feedback to find items that will be bought by a user. This phenomenon is also observed in our empirical studies.
Similarly, we again adopt BPR [
With the learned model parameters ΘP , we can predict the preference of each item i in the candidate list of each user u, and then re-rank the items in the list. The refined list is expected to better represent the purchase likelihood of a certain user, i.e., the recommendation may be more accurate, which is also verified in our empirical studies. We illustrate the effect of the difference between those two ranked lists in Figure 1.
For the optimization problems in the aforementioned two
learning tasks, we can apply stochastic gradient descent to
learn the model parameters [
In our empirical studies, in order to directly compare our
RBPR with the very recent algorithm for HOCCF, i.e.,
TJSL [
ML10M is a public data with about 10 million numerical ratings in {0.5, 1, 1.5, ..., 4.5, 5}, and Netflix is the dataset used in the famous $100 Million competition with about 0.1 billion scores in {1, 2, 3, 4, 5}. For both ML10M and Netflix, we first divide the data into five parts with equal numbers of (u, i, rui) triples, we then take one part and keep the (u, i) pairs with rui = 5 as purchases for training, take one part and keep the (u, i) pairs with rui = 5 as purchases for validation, and take one part and keep the (u, i) pairs with rui = 5 as purchases for test, and finally take the remaining two parts and keep all the (u, i) pairs as browses. We repeat this procedure for three times in order to obtain three copies of data.
We put the statistics of the datasets in Table 1.
For evaluation, we use five ranking-oriented metrics, including Precision@5, Recall@5, F1@5, NDCG@5 and 1-call@5. 3.2
Because HOCCF is a relatively new recommendation
problem, very few solutions have been proposed. In our empirical
studies, we thus include the very recent algorithm TJSL [
We report the recommendation performance in Table 2. We can have the following observations: • RBPR and TJSL are better than BPR in all cases including five evaluation metrics and five datasets, which clearly shows that the feedback browses are useful for learning and mining users’ hidden preferences, and RBPR and TJSL are able to make use of users’ heterogeneous feedback well. • RBPR and TJSL are comparable on three small datasets, e.g., TJSL is the best on ML100K, RBPR is the best on ML1M, and TJSL and RBPR are comparable on Alibaba2015. • TJSL is too slow to generate recommendations on two large datasets within 24 hours, while RBPR can produce significantly better results than BPR, which shows that our RBPR is a more practical solution regarding the efficiency.
The overall performance in Table 2 shows that our RBPR performs the best in making use of the heterogeneous oneclass feedback.
In order to check the performance improvement of our two-stage role-based preference learning solution, we also check the performance of the generated candidate items as shown in Figure 1. Specifically, we denote the method for generating the candidates as RBPR(Browser) since it is based on the role of browser only, and the final recommendation as RBPR(Browser,Purchaser). We report the performance on Precision and NDCG in Figure 3 (other metrics are similar), from which we can see that the second stage of candidate refinement using the purchase data can significantly improve the performance. The improvement also verifies our main assumption that there are usually two separate stages for a user’s shopping action, i.e., browse and purchase.
Method BPR TJSL RBPR BPR TJSL RBPR BPR TJSL RBPR BPR TJSL RBPR BPR TJSL RBPR 0.0552±0.0006 0.0697±0.0016 0.0654±0.0013 0.0928±0.0008 0.1012±0.0011 0.1086±0.0009 0.0050±0.0006 0.0071±0.0004 0.0076±0.0005 RBPR(Browser) RBPR(Browser,Purchaser) RBPR(Browser) RBPR(Browser,Purchaser) 0 ML100K
ML1M Alibaba2015 ML10M
Dataset
Netflix
In this paper, we study an important recommendation
problem called heterogeneous one-class collaborative
filtering (HOCCF) from a novel perspective of users’ roles.
Specifically, we propose a novel role-based preference learning
framework, i.e., role-based Bayesian personalized ranking (RBPR),
based on a seminal work [
For future work, we are interested in extending and applying our role-based preference learning framework to other recommendation settings with more types of users’ roles such as searcher, browser, purchaser, rater and friends.