Incorporating User-Item Similarity in Hybrid Neighborhood-based Recommendation System

Modern hybrid recommendation systems require a sufficient amount of data . However , several internet privacy issues make users skeptical about sharing their personal information with online service providers . This work introduces various novel methods utilizing the baseline estimate to learn user interests from their interactions . Subsequently , extracted user feature vectors are implemented to estimate the user-item correlations , providing an additional fine-tuning factor for neighborhood-based collaborative filtering systems . Comprehensive experiments show that utilizing the user-item similarity can boost the accuracy of hybrid neighborhood-based systems by at least 2.11 % while minimizing the need for tracking users ’ digital footprints . 1 INTRODUCTION . The continuously accelerated growth of communication technology and data storage in the past decades has benefited customers with an enormous amount of online multimedia content such as movies , music , news , and articles , creating billion-dollar industries . Following this evolution , recommendation systems ( RSs ) have been widely developed to automatically help users filter redundant information and suggest only suitable products that fit their needs . Such systems are used in a variety of domains and have become a part of our daily online experience ( Ricci et al. , 2015 ) . RSs are commonly classified into three main types ( Adomavicius & Tuzhilin , 2005 ) : the contentbased technique , the collaborative filtering technique , and the hybrid technique . The content-based approach , as in Lang ( 1995 ) ; Pazzani & Billsus ( 1997 ) ; Lops et al . ( 2011 ) ; Narducci et al . ( 2016 ) , learns to recommend items that are similar to the ones that a user liked based on items ’ features . The main weakness of this approach is the lack of available and reliable metadata associated with item ( Mooney & Roy , 2000 ) . Meanwhile , the collaborative filtering ( CF ) approach does not require product information but only relies on users ’ interaction history which can be either explicit or implicit feedback . CF systems can be divided into two major categories : i ) neighborhood-based models which suggest items that are most similar to the item that a user is interested in ( Herlocker et al. , 2000 ; Tintarev & Masthoff , 2007 ) , and ii ) matrix factorization models which could explore the latent factors connecting items to users in order to make accurate recommendations ( Ricci et al. , 2015 ; Koren et al. , 2009 ; Koren , 2008 ) . However , it is often the case that there is not enough transaction data to make accurate recommendations for a new user or item . To tackle this so-called cold-start problem , hybrid methods are proposed by combining auxiliary information into CF models ( Singh & Gordon , 2008 ; Agarwal & Chen , 2009 ; Wang & Blei , 2011 ; Li et al. , 2011 ; Rendle , 2010 ) . In the interest of the hybrid approach and its advantages , our study attempts to improve typical neighborhood-based RSs utilizing available content-related knowledge . The main contributions of this work are summarized as follows : • Introducing new methods to represent user preference via combining user ’ s interaction data and item ’ s content-based information , which helps to estimate the similarity between a user and an item . • Integrating the user-item similarity degree into the baseline estimate of neighborhood-based RSs to provide more precise recommendations , surpassing competitive hybrid models . The remainder of this paper is organized as follows . Section 2 reviews the basic knowledge on neighborhood-based CF systems , including hybrid models . Detail descriptions of our proposed methods are presented in Section 4 . Section 5 gives experimental results and in-depth analysis . At last , we conclude this study in Section 6 . 2 PRELIMINARIES . In this paper , u , v denote users and i , j denote items . rui denotes the preference by user u for item i , also known as the rating , where high values indicate strong preference , and all the ( u , i ) pairs are stored in the set K = { ( u , i ) |rui is known } . Meanwhile , R ( u ) denotes the set of all items rated by user u . In rating prediction task , the objective is to predict unknown rating r̂ui where user u has not rated item i yet . Popular neighborhood-based CF techniques for the rating prediction task and an existing hybrid variant are briefly reviewed as follows . 2.1 NEIGHBORHOOD-BASED MODELS . The neighborhood-based approach is one of the most popular techniques in CF , which is only based on the similarity between users or items to give recommendations . There are currently two methods for implementing neighborhood-based CF models : i ) user-oriented ( or user-user ) model which predicts a user ’ s preference based on similar users , and ii ) item-oriented ( or item-item ) model which finds similar items to the item a user liked and recommends these items to her . Of the two methods , the latter introduced by Sarwar et al . ( 2001 ) has become dominant . This is due to the fact that the number of users in real-life systems is often orders of magnitude bigger than of items , which makes the user-oriented model inefficient . Furthermore , the item-oriented model is capable of providing a rational explanation for recommendations ( Ricci et al. , 2015 ) . Therefore , our implementations in this work adopt the item-item approach as the base model . The fundamental of neighborhood-based models is similarity measure . By computing the similarity degree sij between all pairs of items i and j using popular similarity measures such as Cosine similarity ( Cos ) or Pearson Correlation Coefficients ( PCC ) , we can identify the set of k neighbors Sk ( i , u ) which consists of k most similar items to i rated by user u . Then , r̂ui can be predicted as a weighted average of the ratings of similar items : r̂ui = ∑ j∈Sk ( i , u ) sijruj∑ j∈Sk ( i , u ) sij ( 1 ) Even though Equation ( 1 ) can capture the user-item interactions , much of the observed ratings are due to the bias effects associated with either users or items , independently of their interactions . In detail , some items usually receive higher ratings than others , and some users tend to give higher ratings than others . kNNBaseline model proposed by Koren ( 2010 ) adjusts the above formula through a baseline estimate which accounts for the user and item effects as follows . r̂kNNBaselineui = bui + ∑ j∈Sk ( i ; u ) sij ( ruj − buj ) ∑ j∈Sk ( i , u ) sij ( 2 ) where bui = µ + bu + bi denotes the baseline estimate , µ denotes the mean of overall ratings , bu and bi correspond to the bias of user u and item i , respectively , which can be trained using popular optimization algorithms such as Stochastic Gradient Descent ( SGD ) or Alternating Least Squares ( ALS ) . 2.2 INTEGRATING CONTENT-BASED INFORMATION INTO NEIGHBORHOOD-BASED MODELS . A number of problems regarding kNN models using similarity measure on the rating information were noticed by Duong et al . ( 2019 ) . The first problem is the sparsity of the rating matrix , which might yield an inaccurate similarity score between two items that share only a few common users . Secondly , filtering common users who rated both items to calculate the similarity score is a timeconsuming task due to a large number of users . To address these problems , a novel similarity measure was proposed using item content-based information . Assuming that each item i is characterized by a feature vector qi = { qi1 , qi2 , ... , qif } ∈ Rf where f is the number of features , which is stored in matrix Q ∈ Rn×f . The value of each element encodes how strong an item exhibits particular properties . The similarity score sij between movies i and j is calculated as follows . sCos content ij = ∑f k=1 qikqjk√∑f k=1 q 2 ik √∑f k=1 q 2 jk ( 3 ) or sPCC content ij = ∑f k=1 ( qik − q̄i ) ( qjk − q̄j ) √∑f k=1 ( qik − q̄i ) 2 √∑f k=1 ( qjk − q̄j ) 2 ( 4 ) where q̄i and q̄j are the mean of feature vectors qi and qj , respectively . Experiments showed that the item-oriented CF models using Coscontent and PCCcontent provide equivalent accuracy to the stateof-the-art CF models using rating information whilst performing at least 2 times faster . Hereafter , kNNBaseline model using one of these similarity measures is referred to as kNNContent . 3 EXPERIMENTAL SETTING . 3.1 MOVIELENS DATASET AND EVALUATION CRITERIA . In this work , the MovieLens 20M dataset is used as a benchmark . This is a widely used dataset in the study of RSs which originally contains 20,000,263 ratings and 465,564 tag applications across 27,278 movies . The ratings are float values ranging from 0.5 to 5.0 with a step of 0.5 . Tag Genome data , which is computed on user-contributed content including tags , ratings , and textual reviews , is firstly introduced in this version of MovieLens dataset ( Harper & Konstan , 2016 ) . To utilize this kind of data , a cleaning process is applied to the dataset . Specifically , the movies without genome tags are excluded from the dataset . Then , only movies and users with at least 20 ratings are kept . Table 1 summarizes the result of the cleaning stage . The preprocessed dataset is split into 2 distinct parts : 80 % as the training set and the remaining 20 % as the testing set . To evaluate the performance of the proposed models , three commonly used indicators in the field of rating prediction are used : RMSE ( Root Mean Squared Error ) and MAE ( Mean Absolute Error ) for accuracy evaluation where smaller values indicate better performance , and Time [ s ] for timing evaluation . Here , RMSE and MAE are defined as follows . RMSE = √ ∑ u , i∈TestSet ( r̂ui − rui ) 2 /|TestSet| ( 5 ) MAE = ∑ u , i∈TestSet |r̂ui − rui| /|TestSet| ( 6 ) where |TestSet| is the size of the testing set . The total duration of the model ’ s learning process on the training set and predicting all samples in the testing set is measured as Time [ s ] . All experiments are carried out using Google Colaboratory with 25GB RAM and no GPU . 3.2 BASELINE MODELS . In this paper , several popular models are selected as baselines to evaluate the proposed methods . Firstly , we implement two competitive neighborhood-based models including kNNBaseline ( Koren , 2010 ) and kNNContent ( Duong et al. , 2019 ) . Besides , SVD ( Funk , 2006 ) and SVD++ ( Koren , 2008 ) , two well-known representatives of matrix factorization technique , are also experimented due to their superior accuracy and flexible scalability with extremely sparse data ( Funk , 2006 ) . Error rates and the time to make predictions are measured for comparison . The optimal hyperparameters for each baseline model are carefully chosen using 5-fold cross-validation . In particular , the error rates of the neighborhood-based models are calculated with the neighborhood size k ∈ { 10 , 15 , 20 , 25 , 30 , 35 , 40 } . Due to better performance compared to Cos and Coscontent , PCC and PCCcontent are chosen as the similarity measures in kNNBaseline and kNNContent models . SVD and SVD++ models are trained using 40 hidden factors with 100 iterations and the step size of 0.002 .

Summary: This paper incorporates user-item similarity score to item-based CF recommender systems.  The user-item similarity score is calculated by Pearson Correlation Coefficient or Cosine Similarity between user features and item features, where item features are tags, and user features are a weighted sum of (user's rated) items' features (tags). The calculated score are used as part of the baseline estimate of the item-based CF method.