Problem In Recommender Systems Research Articles

User profile as a bridge in cross-domain recommender systems for sparsity reduction

In the past two decades, recommender systems have been successfully applied in many e-commerce companies. One of the promising techniques to generate personalized recommendations is collaborative filtering. However, it suffers from sparsity problem. Alleviating this problem, cross-domain recommender systems came into existence in which transfer learning mechanism is applied to exploit the knowledge from other related domains. While applying transfer learning, some information should overlap between source and target domains. Several attempts have been made to enhance the performance of collaborative filtering with the help of other related domains in cross-domain recommender systems framework. Although exploiting the knowledge from other domains is still challenging and open problem in recommender systems. In this paper, we propose a method namely User Profile as a Bridge in Cross-domain Recommender Systems (UP-CDRSs) for transferring knowledge between domains through user profile. Firstly, we build a user profile using demographical information of a user, explicit ratings and content information of user-rated items. Thereafter, the probabilistic graphical model is employed to learn latent factors of users and items in both domains by maximizing posterior probability. At last prediction on unrated item is estimated by an inner product of corresponding latent factors of users and items. Validating of our proposed UP-CDRSs method, we conduct series of experiments on various sparsity levels using cross-domain dataset. The results demonstrate that our proposed method substantially outperforms other without and with transfer learning methods in terms of accuracy.

Transfer to Rank for Heterogeneous One-Class Collaborative Filtering

Heterogeneous one-class collaborative filtering is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this article, we propose to model purchases and browses from a new perspective, i.e., users’ roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed role-based transfer to rank (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e., mixer ) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on three large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.

MMALFM

Personalized rating prediction is an important research problem in recommender systems. Although the latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this article, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users’ preferences and items’ features from different aspects , and also estimate the aspect importance of a user toward an item. Then, the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user’s and item’s latent factors based on ratings. In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects in MATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user’s preferences and the targeted item’s features. Therefore, it is expected that the proposed method can model a user’s preferences on an item more accurately for each user-item pair. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets. Results show that (1) our method achieves significant improvement compared to strong baseline methods, especially for users with only few ratings; (2) item visual features can improve the prediction performance—the effects of item image features on improving the prediction results depend on the importance of the visual features for the items; and (3) our model can explicitly interpret the predicted results in great detail.

Taxonomy-aware collaborative denoising autoencoder for personalized recommendation

Taxonomies are ubiquitous in many real-world recommendation scenarios where each item is classified into a category of a predefined hierarchical taxonomy and provide important auxiliary information for inferring user preferences. However, traditional collaborative filtering approaches have focused on user-item interactions (e.g., ratings) and neglected the impact of taxonomy information on recommendation. In this paper, we present a taxonomy-aware denoising autoencoder based model which incorporates taxonomy-aware side information into denoising autoencoder based recommendation models to enhance recommendation accuracy and alleviate data sparsity and cold start problems in recommendation systems. We propose two types of taxonomic side information, namely the topological representation of tree-structured taxonomy and the statistical properties of the taxonomy. By integrating taxonomic side information, our model can learn more effective user latent vectors which are not only determined by user ratings but also rely on the taxonomy information. We conduct a comprehensive set of experiments on two real-world datasets which provide several outcomes: first, our proposed taxonomy-aware method outperforms the baseline method on RMSE metric. Next, information extracted from taxonomy can help alleviate data sparsity and cold start problems. Moreover, we conduct supplementary experiments to explore the reason why our proposed taxonomic side information improves recommendation performance.

An Improved Matrix Approximation for Recommender Systems Based on Context-Information and Transfer Learning

As we all known, transfer learning is an effective way to alleviate the sparsity problem in recommender systems by transferring the shared knowledge cross multiple related domains. However, additional related domain is not always available, and auxiliary data may be noisy and this leads to negative transfer. In this paper, we suppose that different parts of one domain also have the shared knowledge and put forward a novel in-domain collaborative filtering framework, which utilizes contextual information to divide an original user-item interaction matrix into some smaller sub-matrices and regards the selected sub-matrices as “multiple domains” to establish transfer learning. The proposed framework no longer needs additional domain information and has a better adaptive ability. Also, considering more actual situation that users may have multiple personalities and items may have diverse attributes, we resort to Rating-Matrix Generative Model (RMGM) to generate the shared cluster-level rating pattern. Experiments on dataset Douban with three different categories demonstrate that the proposed framework can improve the prediction accuracy as well as the top-N recommendation performance.

Neural Binary Representation Learning for Large-Scale Collaborative Filtering

Integrating hashing into collaborative filtering (CF) has been a promising solution to address the efficiency problem of large-scale recommender systems, i.e., hashing users and items into binary codes and then making recommendations in Hamming space. However, most of the existing hashing methods for CF solely focus on modeling the user-item similarity (a.k.a. preference) but omit the user-user and item-item similarities, which cannot well preserve the original geometry in the vector space. Another tough issue is how to effectively tackle the encoding loss from the continuous vector space to the discrete Hamming space. In this paper, we propose a neural binary representation (NBR) learning approach by combining hashing with a neural network for the large-scale CF tasks. Our NBR problem is formulated as a Hamming similarity loss plus two anchor smoothing terms, which jointly preserve the intrinsic user-item, user-user, and item-item similarities. To compensate the encoding loss introduced by hashing, the anchors for users and items are pre-learned using both user-item interactions and the side-information of users and items through an AutoEncoder model. In particular, to solve the NBR problem, we develop a computationally efficient algorithm, which learns the binary codes in a fast bit-by-bit way and achieves less quantization deviation than the conventional CF hashing schemes. The extensive experiments on three benchmarks validate the superiority of our NBR approach in comparison to the state-of-the-art CF hashing methods.

Injection: Toward Effective Collaborative Filtering Using Uninteresting Items

We develop a novel framework, named as $l$ -injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top- N recommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion of pre-use preferences of users toward a vast amount of unrated items. Using this notion, we identify uninteresting items that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our proposed approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through comprehensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution consistently and universally enhances the accuracies of existing CF algorithms (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution improves the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in the experiments are available at: https://goo.gl/KUrmip .

Collaborative Filtering: Data Sparsity Challenges

Today internet is a place where the huge amount of data is stored, there is need to sift, which create a problem for the internet user, so recommend system solve the problem. A recommendation system is a system that helps a user found the products and content by forecast the user’s rating of each item and showing them the items that they would rate highly. Recommendation systems are everywhere. With online shopping, customer has nearly infinite choices. No one has enough time to try every product for sale. Recommendation systems play an important role to solve the users search the products and content they care about. Recommendation system is a process of filtering the information that deal with information overloaded problems. Recommendation system is important for both user and service provider. It reduces the cost of transaction and selecting item in an online scenario it also improve the quality of decision making process. It is now an effective means for selling their product. So over emphasized of user is not good for recommendation system. To solve the problems of recommendation system like data sparsity we use one of best technique that is collaborative filtering technique.

Anti-cancer Drug Response Prediction Using Neighbor-Based Collaborative Filtering with Global Effect Removal

Patients of the same cancer may differ in their responses to a specific medical therapy. Identification of predictive molecular features for drug sensitivity holds the key in the era of precision medicine. Human cell lines have harbored most of the same genetic changes found in patients’ tumors and thus are widely used in the research of drug response. In this work, we formulated drug-response prediction as a recommender system problem and then adopted a neighbor-based collaborative filtering with global effect removal (NCFGER) method to estimate anti-cancer drug responses of cell lines by integrating cell-line similarity networks and drug similarity networks based on the fact that similar cell lines and similar drugs exhibit similar responses. Specifically, we removed the global effect in the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. We then used the K most similar neighbors (hybrid of cell-line-oriented and drug-oriented) in the available responses to predict the unknown ones. Through 10-fold cross-validation, this approach was shown to reach accurate and reproducible outcomes of drug sensitivity. We also discussed the biological outcomes based on the newly predicted response values.

A Hybrid Interpolation Weighted Collaborative Filtering Method for Anti-cancer Drug Response Prediction.

Individualized therapies ask for the most effective regimen for each patient, while the patients' response may differ from each other. However, it is impossible to clinically evaluate each patient's response due to the large population. Human cell lines have harbored most of the same genetic changes found in patients' tumors, thus are widely used to help understand initial responses of drugs. Based on the more credible assumption that similar cell lines and similar drugs exhibit similar responses, we formulated drug response prediction as a recommender system problem, and then adopted a hybrid interpolation weighted collaborative filtering (HIWCF) method to predict anti-cancer drug responses of cell lines by incorporating cell line similarity and drug similarity shown from gene expression profiles, drug chemical structure as well as drug response similarity. Specifically, we estimated the baseline based on the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. The similarity scores were then shrunk and weighted by the correlation coefficients drawn from the know response between each pair. Before used to find the K most similar neighbors for further prediction, they went through the case amplification strategy to emphasize high similarity and neglect low similarity. In the last step for prediction, cell line-oriented and drug-oriented collaborative filtering models were carried out, and the average of predicted values from both models was used as the final predicted sensitivity. Through 10-fold cross validation, this approach was shown to reach accurate and reproducible outcome for those missing drug sensitivities. We also found that the drug response similarity between cell lines or drugs may play important role in the prediction. Finally, we discussed the biological outcomes based on the newly predicted response values in GDSC dataset.

Artificial Neural Networks and Particle Swarm Optimization Algorithms for Preference Prediction in Multi-Criteria Recommender Systems

Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference of user on an item. These techniques have several limitations as the preference of the user towards items may depend on several attributes of the items. Multi-criteria recommender systems extend the single rating recommendation techniques to incorporate multiple criteria ratings for improving recommendation accuracy. However, modeling the criteria ratings in multi-criteria recommender systems to determine the overall preferences of users has been considered as one of the major challenges in multi-criteria recommender systems. In other words, how to additionally take the multi-criteria rating information into account during the recommendation process is one of the problems of multi-criteria recommender systems. This article presents a methodological framework that trains artificial neural networks with particle swarm optimization algorithms and uses the neural networks for integrating the multi-criteria rating information and determining the preferences of users. The proposed neural network-based multi-criteria recommender system is integrated with k-nearest neighborhood collaborative filtering for predicting unknown criteria ratings. The proposed approach has been tested with a multi-criteria dataset for recommending movies to users. The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems.

Recommender systems based on multiple social networks correlation

As development of social networks, social recommendation method, an effective information filtering technology, based on sociology rule and network theory, has improved performance of recommendation system and cold start problem. For data deep fusion and diverse development of social platform, the social relationship between users becomes more and more complex. The complexity of multiple social networks challenges social recommendation. However, most existing social recommendation methods focus on single social network, and multi-layer recommendation methods ignore nonlinearity and coupling between different social relationships. To tackle these problems, we propose a probabilistic matrix factorization model for multiply social networks joint recommendation framework based on joint probability distribution. This model analyzes different types of classic social networks and distribution function of user preferences similarity. Then we present unified model of recommendation based on social networks, as well as extensible multiply social networks joint recommendation method. The experimental results demonstrate comparing with relevant social recommendation algorithms; our method performs better on some evaluation indexes such as accuracy and errors.

Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering

Addressing Cold Start Problem in Recommendation Systems with Collaborative filtering and Reverse Collaborative Filtering

Exploiting Emotion on Reviews for Recommender Systems

Review history is widely used by recommender systems to infer users' preferences and help find the potential interests from the huge volumes of data, whereas it also brings in great concerns on the sparsity and cold-start problems due to its inadequacy. Psychology and sociology research has shown that emotion information is a strong indicator for users' preferences. Meanwhile, with the fast development of online services, users are willing to express their emotion on others' reviews, which makes the emotion information pervasively available. Besides, recent research shows that the number of emotion on reviews is always much larger than the number of reviews. Therefore incorporating emotion on reviews may help to alleviate the data sparsity and cold-start problems for recommender systems. In this paper, we provide a principled and mathematical way to exploit both positive and negative emotion on reviews, and propose a novel framework MIRROR, exploiting eMotIon on Reviews for RecOmmendeR systems from both global and local perspectives. Empirical results on real-world datasets demonstrate the effectiveness of our proposed framework and further experiments are conducted to understand how emotion on reviews works for the proposed framework.

Large-scale asynchronous distributed learning based on parameter exchanges

In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies, as each machine begins its new computations after receiving an aggregated information from a master and any delay in sending local information to the latter may be a bottleneck. In this paper, we propose an effective asynchronous distributed framework for the minimization of a sum of smooth functions, where each machine performs iterations in parallel on its local function and updates a shared parameter asynchronously. In this way, all machines can continuously work even though they do not have the latest version of the shared parameter. We prove the convergence of the consistency of this general distributed asynchronous method for gradient iterations and then show its efficiency on the matrix factorization problem for recommender systems and on binary classification.

Temporal Matrix Factorization for Tracking Concept Drift in Individual User Preferences

The matrix factorization (MF) technique has been widely adopted for solving the rating prediction problem in recommender systems. The MF technique utilizes the latent factor model to obtain static user preferences (user latent vectors) and item characteristics (item latent vectors) based on historical rating data. However, in the real world, user preferences are not static but full of dynamics. Though there are several previous works that addressed this time-varying issue of user preferences, it seems (to the best of our knowledge) that none of them are specifically designed for tracking concept drift in individual user preferences . Motivated by this, we develop a temporal MF approach for tracking concept drift in each individual user latent vector. There are two key innovative steps in our approach: 1) we develop a modified stochastic gradient descent method to learn an individual user latent vector at each time step and 2) by Lasso regression, we learn a linear model for the transition of the individual user latent vectors. We test our method on a synthetic data set and several real data sets. In comparison with the original MF, our experimental results show that our temporal method is able to achieve lower root mean square errors (RMSEs) for both the synthetic and real data sets. One interesting finding is that the performance gain in RMSE is mostly from those users who indeed have concept drift in their user latent vectors at the time of prediction. In particular, for the synthetic data set and the Ciao data set, there are quite a few users with that property and the performance gains for these two data sets are roughly 20% and 5%, respectively.

Patch Matching for Image Denoising Using Neighborhood-Based Collaborative Filtering

We consider patch matching as a recommendation system problem and introduce a new patch-matching approach using nearest neighbor-based collaborative filtering (NN-CF). Our approach involves recommending similar patches to a query patch with the help of other similar patches in a noisy image or an external database. Using user-oriented and item-oriented formulations of NN-CF, we present two variations of CF-based patch-matching criterion. To demonstrate the superior matches found with our method, we apply the new patch-matching scheme to patch-based image denoising and evaluate its effect on the denoising performance. We test the methods on two data sets with varying background and image complexities and under different levels of noise. The proposed method not only improves robustness to patch matching but also provides a new formulation to seamlessly combine internal and external denoising.

Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems

Successful Location-Based Services should offer accurate and timely information consumption recommendations to their customers, relevant to their contextual situation. To achieve this and provide the best available recommendations to the user, researchers and developers analyse available data via exploiting data mining techniques. Unfortunately, in some cases, due to lack of available data (e.g. a relatively new member with a limited history) the above technologies and methods are not very effective. In this paper, a novel hybrid approach to alleviate the above-mentioned problem, known as cold start in context-aware recommender systems, is presented. This work aims to help researchers and developers to cope with this problem by combining a) community created knowledge, b) ontologies c) association rule mining and d) an innovative scoring function based on probability metrics.

Recommendation using a clustering algorithm based on a hybrid features selection method

The cold start problem is a potentiel problem in Recommender Systems (RSs). It concerns the inability of the system to infer recommendaation for new users or new items about wich it has not enough iformation. Specifically, when an item is new, the system may fail to perform well due to the insufficiency of available information for this item. The most common solution addressed in the literature consists in combining the content and collaborative information under a single RS. However these hybrid solutions inherit the classical problems of natural language ambiguity and don’t exploit semantic knowledge in their items representations. In this paper, we propose a hybrid RS composed of three modules to surpass those weaknesses. The first one is rested on a powerful content clustering algorithm; which uses a Hybrid Features Selection Method (HFSM). It combines statistical and semantic relevant features to get the maximum profit from the content of items. The second module is the Collaborative Filtering (CF) one, which depends only on users’ ratings. The third one combines the previous modules to solve the problem of missing values in CF approach and to handle new-item issue. The proposed hybrid Recommender is evaluated against traditional item-based CF in different settings: no cold-start situation and a simulation of a new-item scenario (an item with few/ no ratings). The conducted experiments show the ability of the proposed hybrid recommender to deliver more accurate predictions for any item and its outperformance on the classical CF approach, which fails in cold-start situations.

Semi-supervised Learning for a New Customer Problem in Recommender System

Semi-supervised Learning for a New Customer Problem in Recommender System