Improving Collaborative Filtering Recommendation

The explosive growth and availability of data on the internet has caused information overload. Searching for a query is not easy in the sources of information available for the interest of an individual user. Collaborative filtering systems recommend items based upon opinions of people with similar tastes. Collaborative filtering overcomes some difficulties faced by traditional information filtering by eliminating the need for computers to understand the content of the items. Further, collaborative filtering can also recommend articles that are not similar in content to items rated in the past as long as like-minded users have rated the items. Collaborative filtering (CF) is one of the most frequently used techniques in personalized recommendation systems. But currently used CF techniques are based on item rating prediction. We proposed an improved personalized recommended CF algorithm. Hybrid recommender systems or content-boosted technologies are quickly produce high quality recommendations. We have explored content-boosted CF technique which analyzes the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze different Memorybased CF and Model-based CF techniques. Finally, we experimentally evaluate our results and compare them. The testing results show that in most cases, the improved algorithm that we put forward can improve recommendation quality.

Computational Intelligence and Neuroscience

The social media has made the world a global world and we, in addition to, as part of physical society, are now part of the virtual society as well. There has been the generation of a large amount of information over the social web. By way of increasing online information, new opportunities emerged, and diverse issues have been raised, which have attracted researchers to address these research problems. In this current age, where online business and e-commerce are part of our daily lives, recommender systems (RSs) are very effective for information filtering. RSs play a significant role in our lives by assisting users in recommending items and services what they may be interesting in to purchase or avail. In this research work, our goal is to predict the users’ ratings for various items, which are an active research area in collaborative filtering (CF). In this work, we have explored various similarity measures based on user-user and item-item rating predictions on different dataset...

Proc. 21st Assoc. for Advancement of Artificial Intelligence (AAAI) Workshop Intelligent Techniques for Web Personalization (ITWP’06), 2006

Nearest-neighbor collaborative filtering (CF) algorithms are gaining widespread acceptance in recommender systems and e-commerce applications. User ratings are not expected to be independent, as users follow trends of similar rating behavior. In terms of Text Mining, this is analogous to the formation of higher-level concepts from plain terms. In this paper, we propose a novel CF algorithm which uses Latent Semantic Indexing (LSI) to detect rating trends and performs recommendations according to them. We perform an ...

With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital role at the success of such businesses in reaching out to new users and promoting their services and products. With the aim of improving the recommendation performance of such an algorithm, this paper proposes a new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. The k-means algorithm and Singular Value Decomposition (SVD) are both used to cluster similar users and reduce the dimensionality. It proposes and evaluates an effective two stage recommender system that can generate accurate and highly efficient recommendations. The experimental results show that this new method significantly improves the performance of the recommendation systems .

International Journal of Electrical and Computer Engineering (IJECE), 2022

Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments' results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.

2005

Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation and SVD based methods provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings where the known preference information does not change with time. However, a number of practical scenarios require dynamic real-time collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel collaborative filtering approach based on a recently proposed weighted co-clustering algorithm [3] that involves simultaneous clustering of users and items. We design incremental and parallel versions of the co-clustering algorithm and use it to build an efficient real-time collaborative filtering framework. Empirical evaluation of our approach on large movie and book rating datasets demonstrates that it is possible to obtain an accuracy comparable to that of the correlation and matrix factorization based approaches at a much lower computational cost.

Recommender systems help to overcome the organized as follows. Section II provides a brief overview of problem of information overload on the Internet by providing collaborative filtering. In Section III, we present related works.

2003

Collaborative Filtering (CF) systems address the problem of making personalized recommendation using knowledge discovery techniques. By predicting user ratings on new items based on historical ratings of other users, CF systems can give reasonable recommendations to new users. Traditionally, user-based CF algorithm can give predictions and recommendations by finding similar users. However these algorithms often suffer from scalability, sparsity and first-rater problems. Recently, some researchers have proposed the item-based CF algorithms, which give predictions based on similar items' ratings. These can alleviate the scalability problems, but these algorithms suffer from the sparsity and first-rater problems. In this paper, we present two algorithms which use semantic similarity to enhance itembased Collaborative Filtering. In both algorithms, we will extract semantic information about items and compute semantic similarity between them. In the first algorithm, we combine the semantic and rating similarities to find most similar items to a target item. In the second algorithm, we use the combined similarity to fill in the original ratings matrix, and then we run the first algorithm on this less sparse ratings matrix. Our experiments show that both algorithms can achieve better prediction accuracy than traditional item-based CF algorithms. Furthermore, the second algorithm can alleviate the sparsity problem.

Neural Information Processing, 2015

As one of the most important techniques in recommender systems, collaborative filtering (CF) generates the recommendations or predictions based on the observed preferences. Most traditional recommender systems fail to discover the latent associations between the same or similar items with different names, which is called synonymy problem. With the rapid increasing number of users and items, the user-item rating data is extremely sparse. Based on the limited number of user ratings, we cannot capture enough information from the user history using the traditional CF techniques, which could reduce the effectiveness of the recommender systems. In this paper, we propose a novel model User-Relation-Item Model (URIM) for CF, which exploits the latent relationship between different user interest domains and item types. By introducing a component named user-item-relation matrix, which reflects the latent major association patterns behind users and items, URIM tackles the synonymy problem, and therefore achieves a significant performance improvement. We compared our method with several state-of-the-art recommendation algorithms on two real-world datasets. Experimental results validate the effectiveness of our model in terms of prediction accuracy (RMSE) and top-N recommendation quality (Recall and Precision). More specifically, URIM reduces the RMSE by nearly 10 % and 5 % on the two datasets, respectively.

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014

Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calculated purely based on rating overlap in this case, without considering explicit properties of users or items involved, limiting their applicability in domains with very sparse rating spaces. In many domains such as movies, news or electronic commerce recommenders, considerable contextual data in text form describing item properties is available along with the rating data, which could be utilized to improve recommendation quality. In this paper, we propose a novel approach to improve standard CF based recommenders by utilizing latent Dirichlet allocation (LDA) to learn latent properties of items, expressed in terms of topic proportions, derived from their textual description. We infer user's topic preferences or persona in the same latent space, based on her historical ratings. While computing similarity between users, we make use of a combined similarity measure involving rating overlap as well as similarity in the latent topic space. This approach alleviates sparsity problem as it allows calculation of similarity between users even if they have not rated any items in common. Our experiments on multiple public datasets indicate that the proposed hybrid approach significantly outperforms standard user Based and item Based CF recommenders in terms of classification accuracy metrics such as precision, recall and f-measure.