Enhancing recommendation systems performance using highly-effective similarity measures

An experimental study on the performance of collaborative filtering based on user reviews for large-scale datasets

Collaborative filtering (CF) approaches generate user recommendations based on user similarities. These similarities are calculated based on the overall (explicit) user ratings. However, in some domains, such ratings may be sparse or unavailable. User reviews can play a significant role in such cases, as implicit ratings can be derived from the reviews using sentiment analysis, a natural language processing technique. However, most current studies calculate the implicit ratings by simply aggregating the scores of all sentiment words appearing in reviews and, thus, ignoring the elements of sentiment degrees and aspects of user reviews. This study addresses this issue by calculating the implicit rating differently, leveraging the rich information in user reviews by using both sentiment words and aspect-sentiment word pairs to enhance the CF performance. It proposes four methods to calculate the implicit ratings on large-scale datasets: the first considers the degree of sentiment words, while the second exploits the aspects by extracting aspect-sentiment word pairs to calculate the implicit ratings. The remaining two methods combine explicit ratings with the implicit ratings generated by the first two methods. The generated ratings are then incorporated into different CF rating prediction algorithms to evaluate their effectiveness in enhancing the CF performance. Evaluative experiments of the proposed methods are conducted on two large-scale datasets: Amazon and Yelp. Results of the experiments show that the proposed ratings improved the accuracy of CF rating prediction algorithms and outperformed the explicit ratings in terms of three predictive accuracy metrics.

Research on Singular Value Decomposition Recommendation Algorithm Based on Data Filling

In the era of big data, the problem of information overload has become increasingly prominent. Recommendation systems are widely studied due to the problem. Due to the sparseness of data, the recommendation effect is not always ideal. To alleviate the problem of data sparsity, a singular value decomposition recommendation algorithm based on data filling is proposed. First, an improved Tanimoto similarity coefficient calculation method is proposed to calculate the similarity, and effective interpolation data is generated for the singular value decomposition model according to the proposed prediction formula. The experimental results show that when using the same dataset MovieLens100K, compared with several commonly used recommendation algorithms, the improved algorithm improves the prediction accuracy of the model, In the best case, RMSE is 10.1% lower than KNNBasic, 7.8% lower than Slope One algorithm, 6.9% lower than SVD algorithm, and 4.8% lower than SVD++ algorithm, verifying that this method can improve the recommendation quality.

Design of Mobile English Teaching Platform Based on Collaborative Filtering Algorithm

With the rapid development of information technology, today's talent training mode is no longer limited to traditional school education. At the same time, with the maturity of portable mobile devices, a new learning method-mobile learning has been born. In this paper, the narrative of data is taken into account in the way of user collaborative filtering recommendation. For prefilling the matrix, the project confidence level also needs to be considered during the whole process. The project confidence level is measured by information entropy model. In the process of correction, it combines with traditional cosine similarity, calculates the user similarity matrix, can budget equalization, and expands the original matrix. After filling the matrix, the user uses the method of similarity calculation, using Pearson similarity and combining with the Euclidean distance correction method. When comparing the particular result prediction with the actual prediction following the completion of the similarity matrix data, all results point to a considerable reduction in MAE and RMSE. The user does not assess the item score to forecast. This demonstrates how this technique may enhance the reliability and consistency of the mobile English system platform.

Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral Groups

Recommender Systems ( RecSys ) provide suggestions in many decision-making processes. Given that groups of people can perform many real-world activities (e.g., a group of people attending a conference looking for a place to dine), the need for recommendations for groups has increased. A wide range of Group Recommender Systems ( GRecSys ) has been developed to aggregate individual preferences to group preferences. We analyze 175 studies related to GRecSys . Previous works evaluate their systems using different types of groups (sizes and cohesiveness), and most of such works focus on testing their systems using only one type of item, called experience goods (EG). As a consequence, it is hard to get consistent conclusions about the performance of GRecSys . We present the aggregation strategies and aggregation functions that GRecSys commonly use to aggregate group members’ preferences. This study experimentally compares the performance (i.e., accuracy, ranking quality, and usefulness) using four metrics (Hit Ratio, nDCG, Diversity, and Coverage) of eight representative RecSys for group recommendations on ephemeral groups. Moreover, we use two different aggregation strategies, ten different aggregation functions, and two different types of items on two types of datasets (Experience Goods (EG) and Search Goods (SG)) containing real-life datasets. The results show that the evaluation of GRecSys needs to use both EG and SG types of data because the different characteristics of datasets lead to different performance. GRecSys using Singular Value Decomposition (SVD) or Neural Collaborative Filtering (NCF) methods work better than others. It is observed that the Average aggregation function is the one that produces better results.

Boolean logic algebra driven similarity measure for text based applications

In Information Retrieval (IR), Data Mining (DM), and Machine Learning (ML), similarity measures have been widely used for text clustering and classification. The similarity measure is the cornerstone upon which the performance of most DM and ML algorithms is completely dependent. Thus, till now, the endeavor in literature for an effective and efficient similarity measure is still immature. Some recently-proposed similarity measures were effective, but have a complex design and suffer from inefficiencies. This work, therefore, develops an effective and efficient similarity measure of a simplistic design for text-based applications. The measure developed in this work is driven by Boolean logic algebra basics (BLAB-SM), which aims at effectively reaching the desired accuracy at the fastest run time as compared to the recently developed state-of-the-art measures. Using the term frequency-inverse document frequency (TF-IDF) schema, the K-nearest neighbor (KNN), and the K-means clustering algorithm, a comprehensive evaluation is presented. The evaluation has been experimentally performed for BLAB-SM against seven similarity measures on two most-popular datasets, Reuters-21 and Web-KB. The experimental results illustrate that BLAB-SM is not only more efficient but also significantly more effective than state-of-the-art similarity measures on both classification and clustering tasks.