Mixture-preference bayesian matrix factorization for implicit feedback datasets

Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.

The two main tasks addressed by collaborative filtering approaches are rating prediction and ranking. Rating prediction models leverage explicit feedback (e.g. ratings), and aim to estimate the rating a user would assign to an unseen item. In contrast, ranking models leverage implicit feedback (e.g. clicks) in order to provide the user with a personalized ranked list of recommended items. Several previous approaches have been proposed that learn from both explicit and implicit feedback to optimize the task of ranking or rating prediction at the level of recommendation algorithm. Yet we argue that these two tasks are not completely separate, but are part of a unified process: a user first interacts with a set of items and then might decide to provide explicit feedback on a subset of items. We propose to bridge the gap between the tasks of rating prediction and ranking through the use of a novel weak supervision approach that unifies both explicit and implicit feedback datasets. The key aspects of the proposed model is that (1) it is applied at the level of data pre-processing and (2) it increases the representation of less popular items in recommendations while maintaining reasonable recommendation performance. Our experimental results - on six datasets covering different types of heterogeneous user's interactions and using a wide range of evaluation metrics - show that, our proposed approach can effectively combine explicit and implicit feedback and improve the effectiveness of the baseline explicit model on the ranking task by covering a broader range of long-tail items.

In this paper, we present our study and characterisation of explicit and implicit feedback on Last.fm, an online music station and recommender service. The dataset consisted of explicit positive feedback (through loved tracks) and implicit positive feedback (the number of times a track is played). As one would expect, our analysis shows that explicit feedback is very scarce. However, we also found that the rate at which a user provides explicit feedback decreases with time, and that overall leaving explicit feedback has a negative effect on the user's behaviour.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

E-commerce platforms usually train their recommender system models to achieve personalized recommendations based on user behavior data. User behavior can be categorized into implicit and explicit feedback. Explicit feedback data have been well studied. However, the implicit feedback data still have many issues, such as the multiple types of behavior data, lack of negative feedback, and lack of the ability to express the real user preference. Targeting these problems of implicit feedback, we propose a TDF-WNSP-WLFM (time decay factor-weight of negative sample possibility-weighted latent factor model) based on the latent factor model for product recommendation. Our method mainly focuses on reconstructing the implicit rating matrix to enable the algorithm to perform better. The TDF-WNSP-WLFM algorithm is tested on two public user behavior datasets from Taobao and REES46, two big e-commerce platforms. Our algorithm compares favorably with other known collaborative filtering methods.

Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to solve the problem, although most of them assume explicit feedback which strongly limits their real-world applicability. While these algorithms apply various loss functions and optimization strategies, the preference modeling under context is less explored due to the lack of tools allowing for easy experimentation with various models. As context dimensions are introduced beyond users and items, the space of possible preference models and the importance of proper modeling largely increases. In this paper we propose a General Factorization Framework (GFF), a single flexible algorithm that takes the preference model as an input and computes latent feature matrices for the input dimensions. GFF allows us to easily experiment with various linear models on any context-aware recommendation task, be it explicit or implicit feedback based. The scaling properties makes it usable under real life circumstances as well. We demonstrate the framework's potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets. We show in our experiments -- performed on five real life, implicit feedback datasets -- that proper preference modelling significantly increases recommendation accuracy, and previously unused models outperform the traditional ones. Novel models in GFF also outperform state-of-the-art factorization algorithms. We also extend the method to be fully compliant to the Multidimensional Dataspace Model, one of the most extensive data models of context-enriched data. Extended GFF allows the seamless incorporation of information into the fac[truncated]

In the recommendation system, compared with the explicit feedback, implicit feedback has the characteristics of large quantity, easier tracking and easier access. Even in many practical application scenarios of the recommendation system, there is only implicit feedback without explicit feedback Based on implicit feedback to recommend there has been a problem, that is usually implicit feedback provides only reflect the noise signal of user preferences, because although the observed interactions at least reflect the user’s interest in the item, but not observed the interaction may be missing data, and is not necessarily negative samples, so it’s natural lack of negative feedback In this work, this paper improves the neural colla-borative filtering algorithm (NCF) by extracting the popularity characteristics as the basis for extracting negative samples from implicit feedback data. To a certain extent, it solves the problem that negative sampling introduces a lot of noise in previous work. At the same time, we found that the gradient vanishing in the training process when using the traditional loss function Binary Cross Entropy (BCE). In order to solve this problem, a new loss function, BCE-Max, was proposed in this paper. Based on the above improvements, we proposed a neural collaborative filtering recommendation algorithm (PNCF) based on popularity characteristics. By comparing the performance of PNCF, NCF, traditional collaborative filtering and some matrix factorization recommendation algorithms in the evaluation indexes such as HR@10, NDCG@10 and avg_popularity@10, we find that PNCF has certain advantages over these recommendation algorithms. Meanwhile, We found that compared with NCF, the experimental results of PNCF improved by about 1.2% and 2% onHR@10 and NDCG@10 respectively, and even improved by about 2% on HR@1 and NDCG@I.

The current study investigates the effects of more explicit versus more implicit corrective feedback on beginner Mandarin learners’ perception and production of Mandarin tones. The effects of oral corrective feedback have been extensively investigated for various domains of second language acquisition (see Mackey & Goo, 2007). For phonological errors, implicit feedback has shown to be particularly salient to learners (Mackey, Gass, & McDonough, 2000) and therefore potentially more effective for tone learning. However, for lower proficiency beginners, explicit corrective feedback has been shown to be more effective (Li, 2009, 2014). Using a mixed methods design, the current study investigated the acquisition of Mandarin tones in beginner, adult learners (n = 41) enrolled in a 14-week, one-on-one, synchronous computer-mediated communication course. Learners were divided into two experimental groups: a more implicit feedback group and a more explicit feedback group. Pretests and posttests assessed changes in tone perception and production. Upon completion of the course, learners in the more implicit feedback group had greater improvement in tone production compared to the more explicit feedback group (d = .75). Both learners and the instructor indicated a preference toward recasts for tone feedback. No statistically significant differences were found for tone perception.

Implicit feedback such as video watch time is commonly seen in many internet products. Though recommender systems with explicit feedback have been abundantly researched, there are not many methods proposed for building recommender systems with implicit feedback. Restricted Boltzmann Machine (RBM), which uses a two-layer graphical model to describe response variables through hidden units, is a promising approach. An RBM model for collaborative filtering was proposed in the literature, but it only deals with categorized ratings, which is explicit feedback. We propose a novel extension of the RBM method for collaborative filtering with implicit feedback. The model parameters can be learned efficiently through the contrastive divergence algorithm. Compared to other methods, the proposed RBM method can directly predict preferences for a new user given feedback on a few items. Also, it protects privacy by keeping user data locally and only sends updated parameters to a central server. The results on real data with several million records show it works superiorly compared to other prevalent methods.

Recommendation systems rely on various definitions of similarities. These definitions while having numerous design factors in different domains help identify and recommend relevant content. For example, similarity between users, or items, are measured based on, but not limited to, explicit feedback such as ratings, thumbs up; or/and implicit feedback such as clicks, views etc; or/and based on composition of item such as tags, metadata etc. In this paper, we explore a similarity model while very intuitive to find similar items using a very common natural law of attraction between bodies, that is gravitational law. We show how the two attributes, relative mass and distance between the bodies, of gravitation law can be interpreted for an effective personalized recommendations; in both spatial and non-spatial domains. Finally, we illustrate the use of distance and mass in a non-spatial domain and we exhibit the accuracy in recommendations against popular baselines.

Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of the unique challenges that recommender systems face when used by a young and diverse population. However, recommender systems for K12 education could provide many benefits for students and teachers, such as the simplification of personalized learning. Some of the issues with K12 educational recommender systems may be solved by the use of deep learning and implicit feedback. As such, we investigated the use of deep recommendation algorithms and implicit feedback for K12 educational recommender systems. To do this, we compared metrics for highly cited traditional and deep recommendation algorithms trained on explicit and implicit data. We found that recommendation algorithms using deep learning as a group do not differ in performance compared to traditional recommendation algorithms. We also found that the use of implicit feedback led to higher performance than using explicit feedback. The best performing algorithm used both deep learning and implicit feedback. We conclude that deep learning can be a benefit for K12 recommender systems, particularly when the ordered sequence of items is carefully accounted for. We also conclude that researchers and developers must carefully consider which feedback contributes the most information for learning.

Recommendation algorithms have been extensively applied in multiple areas. However, user-level uncertainty in implicit feedback introduce false-positive signals, misaligning interactions with true preferences and introducing noise. This makes it challenging for traditional recommendation methods to achieve both robust and accurate performance. In this manuscript, we propose a semi-supervised co-training algorithm for denoising learning in the recommender system, which incorporates both supervised learning and semi-supervised learning in a cooperative way. We first adopt a Gaussian Mixture Model as a confidence estimation module to partition the implicit feedback into two disjoint datasets: a reliable dataset and an unreliable dataset, based on the intrinsic features of the feedback. To achieve doubly robust learning, we design an iterative cross co-training module that consists of two key components: a two-stage reliable learning process and a cross-sample transmission mechanism. The two-stage reliable learning process enables semi-supervised training on both the reliable and unreliable datasets. Meanwhile, the cross-sample transmission mechanism iteratively transfers hard yet clean samples between dual networks, enhancing robustness through mutual supervision during joint learning. Extensive experiments are conducted using clean test sets extracted from four real-world explicit feedback datasets to validate our denoising method, which demonstrates superior effectiveness across all datasets.

The problem of interest is how to dynamically allocate wireless access services in a competitive market which implements a take-it-or-leave-it allocation mechanism. In this paper we focus on the subproblem of preference elicitation, given a mechanism. The user, due to a number of cognitive and technical reasons, is assumed to be initially uninformed over their preferences in the wireless domain. The solution we have developed is a closed-loop user-agent system that assists the user in application, task and context dependent service provisioning by adaptively and interactively learning to select the best wireless data service. The agent learns an incrementally revealed user preference model given explicit or implicit feedback on its decisions by the user. We model this closed-loop system as a Markov Decision Process, where the agent actions are rewarded by the user, and show how a reinforcement learning algorithm can be used to learn a model of the userýs preferences on-line in the given allocation mechanism. We evaluate the performance and value of the agent in a series of preliminary empirical user studies.

In this study it was attempted to investigate whether different CF strategies, including implicit, explicit, and emergent CF, can differently affect the accuracy and fluency of oral production among Iranian EFL learners. In addition, it explored the learners' attitudes towards how they felt about the CF types applied in the classroom. For these purposes, the researchers selected 54 homogeneous pre-intermediate learners on the basis of the PET results and randomly assigned them into three experimental groups: the Implicit group, the Explicit group, and the Emergent group. While the Implicit and Explicit groups received just implicit feedback and explicit correction for their erroneous oral production, respectively, the Emergent group took CF from implicit to explicit. Oral narrative and picture description tasks and semi-structured interviews were used to collect the quantitative and qualitative data for the study. The results of inferential statistics indicated significant differences among the feedback types in both past-tense and future-tense accuracy. Moreover, the Emergent group had a better performance compared with the other groups with respect to accuracy. However, no significant difference was revealed among the feedback types with respect to fluency. The results of the content analysis also indicated that the learners mostly preferred to receive emergent feedback and participate in the process of error correction. The findings of this study can raise researchers', teachers', and teacher trainers' awareness of the function of various CF types.

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency.To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD.