Increasing the Effectiveness of Prediction in Recommendation Engines Based on Collaborative Filtering

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based models are representative solutions in recommender systems; however, the content-based model has some shortcomings, such as single kind of recommendation results and lack of effective perception of user preferences, while for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this paper, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

Purpose – The purpose of this paper is not only to design a more effective recommendation system for libraries, but also to eliminate many of the weaknesses found in the existing library recommender systems.Design/methodology/approach – A novel library recommender system was designed for English collections by integrating personal ontology model and collaborative filtering model with domain specification.Findings – The trend of the traditional library is evolving toward that of digital library. The personal ontology recommender (PORE) system offers a friendly user interface and provides several personalized services.Research limitations/implications – This system is only implemented and tested in the Library of National Chung Hsing University in Taiwan.Originality/value – The paper demonstrates a good methodology to offer an active, effective, and personalized recommendation system for library patrons.

A location-based orientation-aware recommender system using IoT smart devices and Social Networks

The increasing demand for personalized information has resulted in the development of the Recommender System (RS). RS has been widely utilized and broadly studied to suggest the interests of users and make an appropriate recommendation. This paper gives an overview of several types of recommendation approaches based on user preferences, ratings, domain knowledge, users demographic data, users context and also lists the advantages and disadvantages of each RS approach. In this paper, we also proposed the movie recommendation based on collaborative filtering and singular value decomposition plus-plus (SVD++). The proposed approach is compared with well-known machine learning approaches namely k nearest neighbor (K-NN), singular value decomposition (SVD) and Co-clustering. The proposed approach is experimentally verified using MovieLens 100 K datasets and error of the RS is measured using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The result shows that the proposed approach gives a lesser error rate with RMSE (0.9201) and MAE (0.7219). This approach also overcomes cold-start, data sparsity problems and provides them relevant items and services.

Information wikis and especially Wikipedia are attracting an increasing attention for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. To the best of our knowledge, no effective personalized content recommendation approach has yet been defined to support informal learning from wikis. Therefore, we propose a personalized content recommendation framework that extrapolates topical navigation graphs from learners' free navigation and integrates them with fuzzy thesauri for automatic and adaptive personalized content recommendations to support informal learning in wikis. We design user studies and conceptual knowledge rubric to evaluate the impact of personalized recommendations on learning from Wikipedia. Results show that the proposed personalized content recommendation framework generates highly relevant recommendations. Evaluation of informal learning reveals that users who use Wikipedia with personalized recommendations can achieve higher scores on conceptual knowledge assessment compared to those who use Wikipedia without recommendations. Learners who use Wikipedia with personalized recommendations are able to utilize larger number of concepts and are able to make comparisons and state relations between concepts.

Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system

A Personalized Recommender System from Probabilistic Relational Model and Users’ Preferences

Collaborative Filtering (CF) is a commonly used technique in recommendation systems. It can promote items of interest to a target user from a large selection of available items. It is divided into two broad classes: memory-based algorithms and model-based algorithms. The latter requires some time to build a model but recommends online items quickly, while the former is time-consuming but does not require pre-building time. Considering the shortcomings of the two types of algorithms, we propose a novel Community-based User domain Collaborative Recommendation Algorithm (CUCRA). The idea comes from the fact that recommendations are usually made by users with similar preferences. The first step is to build a user-user social network based on users' preference data. The second step is to find communities with similar user preferences using a community detective algorithm. Finally, items are recommended to users by applying collaborative filtering on communities. Because we recommend items to users in communities instead of to an entire social network, the method has perfect online performance. Applying this method to a collaborative tagging system, experimental results show that the recommendation accuracy of CUCRA is relatively good, and the online time-complexity reduces to O(n).

Long-term performance of collaborative filtering based recommenders in temporally evolving systems

The explosive growth of the Internet has led to an unprecedented increase in available information, creating a major challenge in identifying content that aligns with user interests. Recommendation systems (RS) address this challenge by providing personalized content recommendations based on user behavior and preferences. This article systematically reviews the evolution of recommendation systems with a focus on the methodologies of machine learning (ML) and deep learning (DL). It highlights core technologies in ML-based RS, such as content-based recommendation, collaborative filtering (CF), and hybrid filtering (HF). The article evaluates the effectiveness of various DL models, including autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN). The review also addresses key challenges faced by RS, such as cold start issues and the need for improved model transparency and interpretability. DL methods are shown to significantly enhance recommendation accuracy by leveraging complex patterns in large-scale data, to improve the user experience. Future research directions include refining data preprocessing, enhancing feature engineering, compressing and accelerating DL models, and improving interpretability through advanced mechanisms. This study provides valuable insights and references for researchers aiming to advance recommendation system design and performance optimization.

Today, advertisement has become a part of media content and it is exposed to viewers in ways such as home shopping, PPL (Product Placement), and T-commerce. Even though media contents and platforms have been developed into user-interactive services, the interaction between advertisement systems and users is still limited. For advertising services to interact with users, a recommendation system should be introduced. Recommendation systems have been studied such as content-based, collaborative filtering, and hybrid methods. Since conventional recommendation systems require high computational complexity in the preference prediction process and do not consider the time factor, there are limitations in performing recommendation considering user preference. In this paper, we propose an advertisement recommendation system that predicts the ATU’s (Active Target User) preference for items in real-time. The proposed system creates PRETREE by modeling ATU's history data with a tree data structure for reducing computational complexity. A recommendation is triggered when the item that the ATU is looking at in the media content is detected. If there are rating records for the item in the history of the ATU, the preferences are predicted on the basis of PRETREE. If there is no history for the item, the proposed system predicts preference on the basis of similar groups and the LCC (Longest Common Category) node at the ATU's PRETREE. Finally, we evaluate the performance of the proposed recommendation system using real- world data.

Collaborative and content-based filtering are the majorly used approaches towards implementing recommender systems which can successfully predict the item(s) to be recommended to users based upon their preferences. Each of the methods has its own advantages and disadvantages that are specific to the situation they are used in. The hybrid approach aims to combine both techniques in multiple ways to optimize results and overcome shortcomings. In this paper, we propose a hybrid approach based recommendation engine to improve accuracy. While content based recommendations are made on the similarity of items’ attributes, collaborative recommendations are based on similarity of users. Collaborative filtering is achieved by matrix factorization technique. One of the most effective algorithms for matrix factorization: Singular Value Decomposition (SVD) has been used to perform collaborative filtering and combined with content based model to predict the item ratings per user. The paper aims to achieve the final result with minimum possible errors as per user preferences and recommended items.

Collaborative filtering creates personalized recommendations by considering ratings entered by users. Collaborative filtering algorithms initially detect users whose likings are alike, by exploring the similarity between ratings that have insofar been submitted. Users having a high degree of similarity regarding their ratings are termed near neighbors, and in order to formulate a recommendation for a user, her near neighbors’ ratings are extracted and form the basis for the recommendation. Collaborative filtering algorithms however exhibit the problem commonly referred to as “gray sheep this pertains to the case where for some users no near neighbors can be identified, and hence no personalized recommendations can be computed. The “gray sheep” problem is more severe in sparse datasets, i.e. datasets where the number of ratings is small, compared to the number of items and users. In this paper, we address the “gray sheep” problem by introducing the concept of virtual near neighbors and a related algorithm for their creation on the basis of the existing ones. We evaluate the proposed algorithm, which is termed as CFVNN, using eight widely used datasets and considering two correlation metrics which are widely used in Collaborative Filtering research, namely the Pearson Correlation Coefficient and the Cosine Similarity. The results show that the proposed algorithm considerably leverages the capability of a Collaborative Filtering system to compute personalized recommendations in the context of sparse datasets, tackling thus efficiently the “gray sheep” problem. In parallel, the CFVNN algorithm achieves improvements in rating prediction quality, as this is expressed through the Mean Absolute Error and the Root Mean Square Error metrics.

Skincare refers to skin care products. These products have different purposes depending on the user's skin type. Over time, public awareness and interest in skincare will increase, leading to a rapid growth of skincare products. Therefore, an Item-Based Collaborative Filtering (CF) method is used to develop a skincare recommendation system. This method will provide personalised recommendations by leveraging the behaviour data of other users with similar preferences and characteristics. This study uses user ratings and preferences for skincare products as data. This data is then used to build a CF model, which will be analysed to calculate user similarity patterns using the cosine similarity matrix. The application of the CF method demonstrates its effectiveness in matching user preferences, resulting in the most relevant product recommendations. This system not only increases the accuracy of recommendations but also helps users find products that meet their skin care needs, as the error rate in the system is 0.245.