An Improved Product Recommendation Method for Collaborative Filtering

Abstract

Collaborative filtering (CF) is the most commonly used technique for online recommendations. CF works by computing the interests of a user by gathering preferences or taste information of other users. In this technique, similar users or items are discovered by exploring the user-item rating matrix. Based on the computed similarity, a prediction is made for the unknown or new product. There are many similarity computation methods, such as the Pearson correlation coefficient (PCC), Jaccard, Mean square difference, Cosine, etc. however, the accuracy of product recommendations using these methods is not very promising. This work introduces an improved product recommendation method for collaborative filtering, which is based on the triangle similarity. However, the downside of triangle similarity is that it only considers the common ratings of users. The proposed similarity measure not only focuses on common ratings but also consider the ratings of those items that are not commonly rated from pairs of users. The obtained similarity is further complemented with the user rating preference (URP) behavior in giving rating preferences. To evaluate the accuracy, experiments are performed on the six commonly used datasets in the field of CF. Experimental results prove that the proposed similarity measure performs well as compared to the existing similarity measures. INDEX TERMS Collaborative filtering, recommender systems, triangle similarity, user preferences.

Improved collaborative filtering algorithm using intuitionistic fuzzy C -means clustering and incorporating time factors

“…Calculate V (0) by Equation 11and 12, and U (0) will be updated to U (1) according to Equation 5 and 6, the best fuzzy classification matrix finally obtained…”

“…It can discover the potential needs of users, and has been widely used due to its strong application value. Currently, collaborative filtering algorithms [1] can be divided into two main categories: memory-based [2][3][4][5] and model-based collaborative filtering [6][7][8], where memory-based collaborative filtering can be further divided into userbased collaborative filtering [2,3], item-based collaborative filtering [4,5], and the combination of the two collaborative filterings [9][10][11]. In memory-based collaborative filtering, the similarities between users and items are calculated based on the users' ratings on items to make recommendations.…”

Sistem Rekomendasi Produk Aplikasi Marketplace Berdasarkan Karakteristik Pembeli Menggunakan Metode User Based Collaborative Filtering

“…Rating Preferences (ITR) Improved triangle similarity complemented with user rating preferences (ITR) merupakan algoritma yang dapat diimplementasikan untuk mendapatkan nilai similarity terhadap dua buah titik, didalam algortitma ITR terdapat dua istilah improved triangle similarity (𝑠𝑖𝑚 𝑇𝑅𝐼𝐴𝑁𝐺𝐿𝐸 ) dan user rating preferences (URP) (Iftikhar et al, 2020). Dalam praktiknya, 𝑠𝑖𝑚 𝑇𝑅𝐼𝐴𝑁𝐺𝐿𝐸 dianggap sebagai peningkatan dari kesamaan Triangle Multiplying Jaccard (TMJ), sehingga Improved Triangle Similarity (ITR) tidak hanya berfokus pada peringkat umum, seperti ukuran TMJ, tetapi juga memperhitungkan peringkat pengguna yang tidak umum [7].…”

Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering

“…The presented algorithm combines a word2vec mechanism with a gradient boosting machine learning architecture to explore the purchased products based on users' click patterns. In [53], a product recommendation method for CF based on the triangle similarity is presented. The similarity metric considers the ratings of both the non-commonly rated items from pairs of users as well as the common rated ones.…”