A Hybrid Recommender Combining User, Item and Interaction Data

Grivolla, Jens; Campo, Diego Martín; Sonsona, Miquel; Pulido, Jose-Miguel; Badía, Toni

doi:10.1109/csci.2014.58

Cited by 12 publications

(12 citation statements)

References 3 publications

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“…Experimental results show that the advantages which are introduced through this recommender system has potential suggestions. J. Grivolla et al (2021) proposed a hybrid recommender system which integrates the demographic of users and the characters of the item set interactions [16]. The proposed system is then evaluated on the MOVIE Lens data which included gene information and the user data which operates to provide tailor made discount to the customers.…”

Section: Literature Surveymentioning

confidence: 99%

Improvised DBSC and transductive SVM for hybrid recommendation systems

Anima¹,

Kumar²

2022

ijhs

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Recommendation Systems play a crucial role in improving the business strategies and providing the customers or users with the choice to opt for a product or service. Given the growth and power of information analytics tools and inclination of data mining techniques, a wide range of recommender systems particularly for the web based users have been evolved over the decade. The conventional “One model – Fit for all” model fails in most of the cases due to the common fact that not all users have same interests and have paved way for tailor made recommender systems. It is hence highly important to provide a personalized framework which can accumulate specific interests of the users. Web data are normally highly sparse in nature and formulating a personalized user profile was a difficult task. The previous methods used user based CF method to address the former issue while Extended Jaccard Similarity (EJS) was introduced to address the latter. Although these methods found to be effective, there were scopes for improvement in the Query processing time and accuracy. To solve this problem the proposed system design an Enhanced DBSCAN and TSVM based hybrid recommendation scheme.

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Section: Literature Surveymentioning

confidence: 99%

Improvised DBSC and transductive SVM for hybrid recommendation systems

Anima¹,

Kumar²

2022

ijhs

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“…Satisfying results in recommendation areas can be produced by collaborative filtering using existing transactions between customers and products, although algorithms have a downside from the cold start problem [16]. Many studies deal with these criteria by introducing an additional algorithm for a specific purpose to improve the current model, which results in a hybrid model combining collaborative filtering with various algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Prior work from Grivolla and Badia [16] investigates a hybrid recommender model that uses collaborative filtering with user-item interactions. It integrates customer demographic and items under offer for coupon recommendation that involves interaction data from customers and items.…”

Section: Related Workmentioning

confidence: 99%

Recommender System for Coupon Discount of E-commerce Applications

Wetprasit

Cao

Seow

2022

2022 5th International Conference on Data Science and Information Technology (DSIT)

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Recommender systems have recently been integrated into many fields to introduce numerous appealing products and help with consumer decisions in item selections.Applying recommender systems to coupon discount applications could improve consumers accessibility to coupons of their interest, leading to increased product sales related to coupons. The research aims to create a recommender system model for coupon suggestions based on customer data interactions. The proposed recommender model is a hybrid model that combines matrix factorization and association rule mining. While matrix factorization utilises customer-item interactions with customer transactions history and information as the implicit feedback, the association rule uses the Apriori algorithm to find frequent itemsets from customer discount transactions to create the weight of each item. As a result, the proposed hybrid recommender model provides overall performance higher than the baseline models and other benchmark models. In addition, the proposed model has a precision rank increase of nearly 20% in the same dataset compared to that of the baseline models.

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“…Examples of user features are user demographics such as age, living area, gender, occupation, etc. [9]. If our goal is to build a movie recommender, then the genre, year of its release, name of the director, can all be interpreted as item characteristics, which can be added to system for making better recommendations.…”

Section: Lda-lfm With Extra Featuresmentioning

confidence: 99%

Utilizing textual reviews in latent factor models for recommender systems

Aslanyan

Frăsincar

2021

Proceedings of the 36th Annual ACM Symposium on Applied Computing

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Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modelling technique (i.e., Latent Factor Model) with a topic modelling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user-and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.

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A Hybrid Recommender Combining User, Item and Interaction Data

Cited by 12 publications

References 3 publications

Improvised DBSC and transductive SVM for hybrid recommendation systems

Improvised DBSC and transductive SVM for hybrid recommendation systems

Recommender System for Coupon Discount of E-commerce Applications

Utilizing textual reviews in latent factor models for recommender systems

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