Matrix Factorization Techniques for Recommender Systems

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Applied Nonparametric Statistics

Mining outliers in database is to find exceptional objects that deviate from the rest of the data set. Besides classical outlier analysis algorithms, recent studies have focused on mining local outliers, i.e., the outliers that have density distribution significantly different from their neighborhood. The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2,11]. However, when outliers are in the location where the density distributions in the neighborhood are significantly different, for example, in the case of objects from a sparse cluster close to a denser cluster, this may result in wrong estimation. To avoid this problem, here we propose a simple but effective measure on local outliers based on a symmetric neighborhood relationship. The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. To compute such local outliers efficiently, several mining algorithms are developed that detects top-n outliers based on our definition. A comprehensive performance evaluation and analysis shows that our methods are not only efficient in the computation but also more effective in ranking outliers.

Ranking outliers using symmetric neighborhood relationship

Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users Therefore, these measures are not suitable in a sparse data In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Existing clustering approaches are usually restricted to crisp clustering, where objects just belong to one cluster; meanwhile there are some applications where objects could belong to more than one cluster. In addition, existing clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed; however many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. In this paper, we propose a new tree-based incremental overlapping clustering method using the three-way decision theory. The tree is constructed from representative points introduced by this paper, which can enhance the relevance of the search result. The overlapping cluster is represented by the three-way decision with interval sets, and the three-way decision strategies are designed to updating the clustering when the data increases. Furthermore, the proposed method can determine the number of clusters during the processing. The experimental results show that it can identifies clusters of arbitrary shapes and does not sacrifice the computing time, and more results of comparison experiments show that the performance of proposed method is better than the compared algorithms in most of cases.

A tree-based incremental overlapping clustering method using the three-way decision theory

https://ccc.inaoep.mx/~ariel/2012/A%20distance%20based%20clustering%20method%20for%20arbitrary%20shaped%20clusters%20in%20large%20datasets.pdf

A distance based clustering method for arbitrary shaped clusters in large datasets

Recommender System (RS) is an essential component of many businesses, especially in e-commerce domain. RS exploits the preference history (rating, purchase, review, etc.) of users in order to provide the recommendations. A user in traditional RS can provide only one rating value about an item. Deep Neural Networks have been used in this single rating system to improve recommendation accuracy in the recent times. However, the single rating systems are inadequate to understand the usersfi preferences about an item. On the other hand, business enterprises such as tourism, e-learning, etc. facilitate users to provide multiple criteria ratings about an item, thus it becomes easier to understand users' preference over single rating system. In this paper, we propose an extended Stacked Autoencoders (a Deep Neural Network technique) to utilize the multi-criteria ratings. The proposed network is designed to learn the relationship between each user's criteria and overall rating efficiently. Experimental results on real world datasets (Yahoo! Movies and TripAdvisor) demonstrate that the proposed approach outperforms state-of-the-art single rating systems and multi-criteria approaches on various performance metrics.

User preference learning in multi-criteria recommendations using stacked auto encoders

Recommender system has been established as an effective tool for users in providing personalized suggestions in many domains, especially in e-commerce. In these domains, recommendations are provided based on the feedback (ratings) given by the users. However, recommendations provided by the traditional approaches are biased towards the popular items (items that receive more number of ratings). As a result, unpopular items are left out and these items remain un-recommended and unsold. These unpopular items form a “long tail” in the product space, resulting in a huge loss to the e-commerce industry. However, diminishing this long tail effect is a highly challenging and non-trivial task due to limited available rating information. Recommending long tail items helps in improving the item liquidation and recommendation diversity as well. In this paper, we propose a novel framework to mitigate the long tail effect and overcome the limited ratings problem using few shot learning techniques. Siamese network, a type of few shot learning technique is found to be performing well in many domains with a limited number of instances in the recent past. In the proposed framework, vital statistics of each user are computed and this information is provided to deep siamese network. The trained siamese network is used to identify the long tail items that are similar to the liked items of each user. Finally, the identified long tail items are recommended to the appropriate users. We introduce three novel performance metrics to evaluate the long tail item recommendations. The proposed framework is evaluated on two real world datasets (MovieLens 1M and Netflix) and the results demonstrate that the proposed framework outperforms the traditional approaches and existing long-tail recommendation techniques.

Mitigating long tail effect in recommendations using few shot learning technique

Cluster analysis in a large dataset is an interesting challenge in many fields of Science and Engineering. One important clustering approach is hierarchical clustering, which outputs hierarchical (nested) structures of a given dataset. The single-link is a distance-based hierarchical clustering method, which can find non-convex (arbitrary)-shaped clusters in a dataset. However, this method cannot be used for clustering large dataset as this method either keeps entire dataset in main memory or scans dataset multiple times from secondary memory of the machine. Both of them are potentially severe problems for cluster analysis in large datasets. One remedy for both problems is to create a summary of a given dataset efficiently, and the summary is subsequently used to speed up clustering methods in large datasets. In this paper, we propose a summarization scheme termed data sphere (ds) to speed up single-link clustering method in large datasets. The ds utilizes sequential leaders clustering method to collect important statistics of a given dataset. The single-link method is modified to work with ds. Modified clustering method is termed as summarized single-link (SSL). The SSL method is considerably faster than the single-link method applied directly to the dataset, and clustering results produced by SSL method are close to the clustering results produced by single-link method. The SSL method outperforms single-link using data bubble (summarization scheme) both in terms of clustering accuracy and computation time. To speed up proposed summarization scheme, a technique is introduced to reduce a large number of distance computations in leaders method. Experimental studies demonstrate effectiveness of the proposed summarization scheme for large datasets.

Bidyut Kr. Patra

Papers

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

A distance based clustering method for arbitrary shaped clusters in large datasets

User preference learning in multi-criteria recommendations using stacked auto encoders

Mitigating long tail effect in recommendations using few shot learning technique

Effective data summarization for hierarchical clustering in large datasets