Improving Prediction Performance of Dynamic Neighbor Selection in User-Based Collaborative Filtering

Recommender systems have become more and more popular in online environments in recent years. Although different approaches are introduced to build a powerful recommender system, collaborative filtering is one of the most used approaches in the recommender systems. Yet, researchers still introduce new methods to improve prediction performances in collaborative filtering. k nearest neighbor algorithm is one of the most dominant and prevalent one in collaborative filtering. The underlying approach behind it is to select a predefined k neighbors for an active user among all users. In the traditional algorithm, the value of k is constant and is determined before the prediction process. Recently, scholars proposed to use dynamic k neighbor selection for each user. Inspired from this work, we propose to improve prediction performance, accuracy and coverage, of collaborative filtering systems under k nearest neighbor approach. We first propose that users who rate the target item should become nominees for dynamic k neighbor selection instead of all possible users whose similarities can be calculated. The similarity calculation is the most crucial point of the k nearest neighbor algorithm. Furthermore, we also propose to use the significance-weighting approach in addition to the traditional Pearson correlation coefficient when identifying the best dynamic k neighbors for each user. The experimental results on the two well-known datasets show that the prediction accuracy and coverage improve in the dynamic k neighbor selection method by selecting neighbors among users who rated the target item and introducing the significance-weighting factor into the neighbor selection phase to find more eligible neighbors.

Kullanıcı Tabanlı İşbirlikçi Filtrelemede Dinamik Komşu Seçiminin Tahmin Performansını Artırma

Öneri sistemleri son yıllarda çevrimiçi ortamlarda giderek daha popüler hale geldi. Güçlü bir öneri sistemi oluşturmak için farklı yaklaşımlar kullanılmasına rağmen, işbirlikçi filtreleme öneri sistemlerinde en çok kullanılan yaklaşımlardan biridir. Buna rağmen araştırmacılar, işbirlikçi filtrelemede tahmin performanslarını iyileştirmek için hala yeni yöntemler sunuyorlar. k en yakın komşu algoritması, işbirlikçi filtrelemede en dominant ve yaygın algoritmalardan biridir. Bu algoritmanın arkasındaki temel yaklaşım, aktif kullanıcı için tüm kullanıcılar arasından önceden tanımlanmış k tane komşu seçmektir. Geleneksel algoritmada k değeri sabittir ve tahmin işleminden önce belirlenir. Son zamanlarda, araştırmacılar her kullanıcı için dinamik k komşu seçimini kullanmayı önermişlerdir. Bu çalışmadan esinlenerek, en yakın komşu yaklaşımı altında işbirlikçi filtreleme sistemlerinin tahmin performansını, doğruluğunu ve kapsamını, iyileştirmeyi öneriyoruz. İlk olarak, benzerlikleri hesaplanabilen olası tüm kullanıcılar yerine hedef ürünü oylayan kullanıcıların dinamik k komşu seçimi için aday olmaları önerilir. Benzerlik hesaplaması, en yakın komşuluk algoritmasının en önemli noktasıdır. Ayrıca, her kullanıcı için en iyi dinamik k komşularını belirlerken geleneksel Pearson korelasyon katsayısına ek olarak önemağırlıklandırma yaklaşımını kullanmayı öneriyoruz. Bilinen iki veri kümesindeki deneysel sonuçlar, daha kalifiye komşular seçmek için komşuları hedef ürünü oylayan kullanıcılar arasından seçmenin ve komşu seçme aşamasına önem-ağırlıklandırma yöntemi eklemenin dinamik k komşu seçimi metodunun tahmin performansını iyileştirdiğini göstermiştir.

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