Mixed similarity learning for recommendation with implicit feedback

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.

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.

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.

Collaborative Filtering with Implicit Feedbacks e.g., browsing or clicking records, named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing works of CF-IF can be mainly classified into two categories, i.e., point-wise regression based and pair-wise ranking based, where the latter one relaxes assumption and usually obtains better performance in empirical studies. In real applications, implicit feedback is often very sparse, causing CF-IF based methods to degrade significantly in recommendation performance. In this case, side information e.g., item content is usually introduced and utilized to address the data sparsity problem. Nevertheless, the latent feature representation learned from side information by topic model may not be very effective when the data is too sparse. To address this problem, we propose collaborative deep ranking CDR, a hybrid pair-wise approach with implicit feedback, which leverages deep feature representation of item content into Bayesian framework of pair-wise ranking model in this paper. The experimental analysis on a real-world dataset shows CDR outperforms three state-of-art methods in terms of recall metric under different sparsity level.

The goal of the recommender system is to understand the user's preferences and make appropriate recommendations for certain behaviors. Similarity is an important part of recommendation algorithm. However, most current recommendation algorithms ignore the social relation on users, and the most similarity calculations are based on the common object, which does not fully exploit the implicit information. For these challenges, firstly, an adaptive approach which takes both trust and rating into account is proposed to measure the implicit similarity between a user and his friends when the user as a truster and a trustee, respectively. Secondly, a recommendation algorithm combining trust and ratings is proposed, which also takes the biases of users and items, and the user's implicit feedback into account to enhance the accuracy of the algorithm. Finally, the social regularization recommendation algorithm is modified. The experimental results on two different datasets show that the proposed algorithm is able to not only outperform the traditional recommendation algorithm, but also effectively alleviate the data sparsity and cold start problems.

Due to the inherent challenges in acquiring explicit feedback, graph collaborative filtering (GCF) models often resort to implicit feedback. However, there exists noise in implicit feedback that may not accurately reflect users’ preferences. This noise will be amplified by the aggregating and propagating operations of GCF, thereby affecting the performance of GCF. Existing noise mitigation methods attempt to filter noisy samples from implicit feedback data, yet they face limitations such as dependency on side information for sample selection, neglect of false-negative noise, and disregard for the impact of previous selections on current iteration. To overcome these challenges, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u>ynamic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</u>raph <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u>earning framework to <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u>enoise implicit feedback for GCF (DGLD). DGLD comprises a graph learning module and a reinforcement learning module. The graph learning module evaluates the confidence degrees of interactions by leveraging user–item cosine similarity and global user preferences, updating user–item interaction graph without relying on side information. Meanwhile, the reinforcement learning module employs a policy network to select “false-positive denoising” and “false-negative denoising” actions based on performance of the recommendation model and state of interaction graph obtained from previous iterations. These modules work synergistically to dynamically denoise implicit feedback. Experimental results on three benchmark datasets underscore the superiority of our approach over state-of-the-art general and denoising recommendation models.

Distinguishing latent interaction types from implicit feedbacks for recommendation

In view of the problem of how to improve the accuracy of education resources recommendation in Internet era, a recommendation algorithm based on implicit feedback and pairwise education resources are proposed. Firstly, the feature extraction of learner-education resource is carried out, and the implicit features of learner-education resource are extracted by implicit semantic model, and the prediction results of factor decomposition are used as input to sorting learning algorithm. Converting sorting problems to classification problems uses the theory of ordering learning. Based on the experiment comparison with the traditional recommendation algorithm, it is compared with the comparison algorithm, which has better effect on the recommendation of educational resources. The algorithm increases by nearly 30% on metrics MAP, NDGG.

Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applications, it is much less researched than the explicit feedback case. State-of-the-art algorithms that are efficient on the explicit case cannot be straightforwardly transformed to the implicit case if scalability should be maintained. There are few if any implicit feedback benchmark datasets, therefore new ideas are usually experimented on explicit benchmarks. In this paper, we propose a generic context-aware implicit feedback recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor factorization learning method that scales linearly with the number of non-zero elements in the tensor. The method also allows us to incorporate diverse context information into the model while maintaining its computational efficiency. In particular, we present two such context-aware implementation variants of iTALS. The first incorporates seasonality and enables to distinguish user behavior in different time intervals. The other views the user history as sequential information and has the ability to recognize usage pattern typical to certain group of items, e.g. to automatically tell apart product types or categories that are typically purchased repetitively (collectibles, grocery goods) or once (household appliances). Experiments performed on three implicit datasets (two proprietary ones and an implicit variant of the Netflix dataset) show that by integrating context-aware information with our factorization framework into the state-of-the-art implicit recommender algorithm the recommendation quality improves significantly.

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this paper, we propose a deep learning (DL) based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly used as input of recommendation algorithms, implicit feedback embedding (IFE) is proposed. IFE converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features. Using IFE could reduce the scale of model parameters conspicuously and increase model training efficiency. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art DL-based recommendation algorithms on both accuracy and training efficiency in terms of quantitative assessments.

When searching for data in a telecommunications network management application, large search result sets are common. In order to refine the results to retrieve useful information existing systems normally require additional user intervention such as appending or removing a search keyword, adding a filter, grouping results, etc. This work proposes a Probabilistic Search Recommendation Algorithm to Improve Search Efficiency for Network Intelligence and Troubleshooting (PRISENIT). PRISENIT is a query-based recommendation algorithm intended to improve search efficiency and usability for telecommunication system management. PRISENIT is an extension of an item-based collaborative filtering algorithm. It uses correlation-based similarity and users' implicit feedback in order to improve search efficiency. It learns from previous experiences in order to optimize decision-making. Currently there exists no known query-based recommender adaptation mechanism for network management. Existing search engines use previous user searches to make a suggestion based on the keyword. PRISENIT not only considers search terms, it also considers the influence of filters and features in order to makes network searches more efficient as it removes the necessity for users to manually choose search features or search filters. Experimental results show that PRISENIT can improve user experience in a telecommunications management environment.

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.

Top-N item recommendation has been a widely studied task from implicit feedback. Although much progress has been made with neural methods, there is increasing concern on appropriate evaluation of recommendation algorithms. In this paper, we revisit alternative experimental settings for evaluating top-N recommendation algorithms, considering three important factors, namely dataset splitting, sampled metrics and domain selection. We select eight representative recommendation algorithms (covering both traditional and neural methods) and construct extensive experiments on a very large dataset. By carefully revisiting different options, we make several important findings on the three factors, which directly provide useful suggestions on how to appropriately set up the experiments for top-N item recommendation.

Social online learning is gaining prominence with the emergence of social media and networks. In the presence of recommendation systems, the learner has access to several educational contents and elements proposed according to his interaction, profile, dynamics and other parameters. Our proposal revolves around an intelligent recommender system for generating recommendations in social learning environments, in particular social networks, based on the learner's activities, falling into the category of hybrid recommender systems and integrating the concept of community detection in the generation of recommendations. The proposed global approach is composed of several recommender systems: the correlation and co-occurrence based recommender system aims at measuring the degree of connectivity between learners' activities in order to generate relevant recommendations in the light of learners' implicit feedback, the community detection based recommender system through friendships. This system is an important part of our overall approach as it combines the hybrid recommendation system with the community detection based on friendship links between learners and the recommendation system based on the degree of interactivity of learners with the available learning objects. This system also consists in detecting communities, which group learners with similar preferences and orientations. The results obtained prove the potential of combining the two notions of connectivity and the integration of community detection based on several indicators to produce more relevant recommendations, while offering recommendations by group of learners by applying the concept of continuous improvement.KeywordsSocial learningRecommender systemCorrelationCo-occurrenceFriendshipInteractionCommunity detection

Recommender systems have become indispensable for online services since they alleviate the information overload problem for users. Some work has been proposed to support the personalized recommendation by utilizing collaborative filtering to learn the latent user and item representations from implicit interactions between users and items. However, most of existing methods simplify the implicit frequency feedback to binary values, which make collaborative filtering unable to accurately learn the latent user and item features. Moreover, the traditional collaborating filtering methods generally use the linear functions to model the interactions between latent features. The expressiveness of linear functions may not be sufficient to capture the complex structure of users’ interactions and degrades the performance of those recommender systems. In this paper, we propose a neural personalized ranking model for collaborative filtering with the implicit frequency feedback. The proposed method integrates the ranking‐based poisson factor model into the neural networks. Specifically, we firstly develop a ranking‐based poisson factor model, which combines the poisson factor model and the Bayesian personalized ranking. This model adopts a pair‐wise learning method to learn the rankings of uses’ preferences between items. After that, we propose a neural personalized ranking model on top of the ranking‐based poisson factor model, named NRPFM, to capture the complex structure of user‐item interactions. NRPFM applies the ranking‐based poisson factor model on neural networks, which endows the linear ranking‐based poisson factor model with a high level of nonlinearities. Experimental results on two real‐world datasets show that our proposed method compares favorably with the state‐of‐the‐art recommendation algorithms.