Item-based Collaborative Filtering Approach Research Articles

Graph-ICF: Item-based collaborative filtering based on graph neural network

Item-based collaborative filtering (ICF) has been widely used in industrial applications due to its good interpretability and flexible composability. The main idea of ICF is to characterize users’ preferences and recommend similar items based on the items interacted by users in the history logs. As such, the key problem for ICF approaches is how to learn item–item similarity effectively. Existing ICF approaches in general obtain an item’s feature by mapping from the embedding matrix, where only the shallow relation between the target item and the user’s historical interactive items can be captured, while the deeper relations conveyed by other users similar to the current user are neglected. To tackle this problem, we propose a graph-based ICF method (Graph-ICF), which takes advantage of the information aggregation and propagation properties of graph structure to dig out the deeper item relations that have been overlooked by existing ICF models. Moreover, existing ICF models only utilize item-level attention to discriminate which item–item similarities are important for a prediction, without considering user’s personalized favor of interacting with an item. To address this limitation, we further propose a feature level attention module in Graph-ICF to distinguish which feature dimensions better reflect the user’s preferences, and thus can yield more personalized recommendation. We conduct extensive experiments on three public benchmarks, demonstrating the superior performance of Graph-ICF over several state-of-the-art ICF models and graph-based CF methods.

Genetic dissection of Striga hermonthica (Del.) Benth. resistance via genome-wide association and genomic prediction in tropical maize germplasm

Key messageGenome-wide association revealed that resistance to Striga hermonthica is influenced by multiple genomic regions with moderate effects. It is possible to increase genetic gains from selection for Striga resistance using genomic prediction.Striga hermonthica (Del.) Benth., commonly known as the purple witchweed or giant witchweed, is a serious problem for maize-dependent smallholder farmers in sub-Saharan Africa. Breeding for Striga resistance in maize is complicated due to limited genetic variation, complexity of resistance and challenges with phenotyping. This study was conducted to (i) evaluate a set of diverse tropical maize lines for their responses to Striga under artificial infestation in three environments in Kenya; (ii) detect quantitative trait loci associated with Striga resistance through genome-wide association study (GWAS); and (iii) evaluate the effectiveness of genomic prediction (GP) of Striga-related traits. An association mapping panel of 380 inbred lines was evaluated in three environments under artificial Striga infestation in replicated trials and genotyped with 278,810 single-nucleotide polymorphism (SNP) markers. Genotypic and genotype x environment variations were significant for measured traits associated with Striga resistance. Heritability estimates were moderate (0.42) to high (0.92) for measured traits. GWAS revealed 57 SNPs significantly associated with Striga resistance indicator traits and grain yield (GY) under artificial Striga infestation with low to moderate effect. A set of 32 candidate genes physically near the significant SNPs with roles in plant defense against biotic stresses were identified. GP with different cross-validations revealed that prediction of performance of lines in new environments is better than prediction of performance of new lines for all traits. Predictions across environments revealed high accuracy for all the traits, while inclusion of GWAS-detected SNPs led to slight increase in the accuracy. The item-based collaborative filtering approach that incorporates related traits evaluated in different environments to predict GY and Striga-related traits outperformed GP for Striga resistance indicator traits. The results demonstrated the polygenic nature of resistance to S. hermonthica, and that implementation of GP in Striga resistance breeding could potentially aid in increasing genetic gain for this important trait.

Genomic Selection in Winter Wheat Breeding Using a Recommender Approach.

Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs.

Performing item-based recommendation for mining multi-source big data by considering various weighting parameters

In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.

Performing item-based recommendation for mining multi-source big data by considering various weighting parameters

In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.

Efficient global correlation measures for a collaborative filtering dataset

Recommender systems based on collaborative filtering (CF) rely on datasets containing users’ taste preferences for various items. Accuracy of various prediction approaches depends on the amount of similarity between users and items in a dataset. As a heuristic estimate of this data quality aspect, which could serve as an indicator of the prediction ability, we define the Global User Correlation Measure (GUCM) and the Global Item Correlation Measure (GICM) of a dataset containing known user–item ratings. The proposed measures range from 0 to 1 and describe the quality of the dataset regarding the user–user and item–item similarities: a higher measure indicates more similar pairs and better prediction ability. The experiments show a correlation between the proposed measures and the accuracy of standard prediction models. The measures can be used to quickly estimate whether a dataset is suitable for collaborative filtering and whether we can expect high prediction accuracy of user-based or item-based CF approaches.

Tourism Destination Recommender System for the Cold Start Problem

With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system

Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services.

Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings

To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that two parties hold ratings for the same users and items simultaneously; however, existing two-party privacy-preserving collaborative filtering solutions do not cover such overlaps. Since rating values and rated items are confidential, overlapping ratings make privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies. We consider both user-based and item-based collaborative filtering approaches and propose novel privacy-preserving collaborative filtering schemes in this sense. We also evaluate our schemes using real movie dataset, and the empirical outcomes show that the parties can promote collaborative services using our schemes.

User-Weight Model for Item-based Recommendation Systems

Nowadays, item-based Collaborative Filtering (CF) has been widely used as an effective way to help people cope with information overload. It computes the item-item similarities/differentials and then selects the most similar items for prediction. A weakness of current typical item-based CF approaches is that all users have the same weight in computing the item relationships. In order to improve the recommendation quality, we incorporate users’ weights based on a relationship model of users into item similarities and differentials computing. In this paper, a model of user relationship, a method for computing users’ weights, and weight-based item-item similarities/differentials computing approaches are proposed for item-based CF recommendations. Finally, we experimentally evaluate our approach for recommendation and compare it to typical item-based CF approaches based on Adjusted Cosine and Slope One. The experiments show that our approaches can improve the recommendation results of them.

Userrank for item-based collaborative filtering recommendation

With the recent explosive growth of the Web, recommendation systems have been widely accepted by users. Item-based Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. A common problem of current item-based CF approaches is that all users have the same weight when computing the item relationships. To improve the quality of recommendations, we incorporate the weight of a user, userrank, into the computation of item similarities and differentials. In this paper, a data model for userrank calculations, a PageRank-based user ranking approach, and a userrank-based item similarities/differentials computing approach are proposed. Finally, the userrank-based approaches improve the recommendation results of the typical Adjusted Cosine and Slope One item-based CF approaches.