scholarly journals Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

2019 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Emelia Opoku Aboagye ◽  
Rajesh Kumar
Keyword(s):  
Deep Learning ◽  
Feature Space ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Tensor Factorization ◽  
Scalable Systems ◽  
Collaborative Recommendation ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Cold Start Problem

We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start Problem

2021 ◽  
pp. 1492-1512
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L.D.
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Filtering Algorithm ◽  
Other Information ◽  
Collaborative Filtering Algorithm ◽  
Real World Datasets ◽  
Gender Based ◽  
Recommender Algorithm ◽  
Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a genetic algorithm-influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of the genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state of art approaches.

scholarly journals A Deep Learning-Based Course Recommender System for Sustainable Development in Education

2021 ◽  
Vol 11 (19) ◽  
pp. 8993
Author(s):  
Qinglong Li ◽  
Jaekyeong Kim
Keyword(s):  
Deep Learning ◽  
Online Education ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Course Content ◽  
User Behaviors ◽  
Real World Datasets ◽  
High Level

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods.

scholarly journals MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation

2021 ◽  
Author(s):  
Huimin Sun ◽  
Jiajie Xu ◽  
Kai Zheng ◽  
Pengpeng Zhao ◽  
Pingfu Chao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Cold Start ◽  
User Preferences ◽  
Location Based Services ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Meta Learning ◽  
Optimized Model ◽  
Real World Datasets ◽  
Cold Start Problem

Next Point-of-Interest (POI) recommendation is of great value for location-based services. Existing solutions mainly rely on extensive observed data and are brittle to users with few interactions. Unfortunately, the problem of few-shot next POI recommendation has not been well studied yet. In this paper, we propose a novel meta-optimized model MFNP, which can rapidly adapt to users with few check-in records. Towards the cold-start problem, it seamlessly integrates carefully designed user-specific and region-specific tasks in meta-learning, such that region-aware user preferences can be captured via a rational fusion of region-independent personal preferences and region-dependent crowd preferences. In modelling region-dependent crowd preferences, a cluster-based adaptive network is adopted to capture shared preferences from similar users for knowledge transfer. Experimental results on two real-world datasets show that our model outperforms the state-of-the-art methods on next POI recommendation for cold-start users.

Genre Familiarity Correlation-Based Recommender Algorithm for New User Cold Start Problem

2021 ◽  
Vol 17 (3) ◽  
pp. 30-49
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L. D.
Keyword(s):  
World Wide ◽  
Cold Start ◽  
The Other ◽  
The World ◽  
Tremendous Amount ◽  
Real World Datasets ◽  
Recommender Algorithm ◽  
The Right ◽  
Cold Start Problem ◽  
Better Than

The advancement of web services paved the way to the accumulation of a tremendous amount of information into the world wide web. The huge pile of information makes it hard for the user to get the required information at the right time. Therefore, to get the right item, recommender systems are emphasized. Recommender algorithms generally act on the user information to render recommendations. In this scenario, when a new user enters the system, it fails in rendering recommendation due to unavailability of user information, resulting in a new user problem. So, in this paper, a movie recommender algorithm is constructed to address the prevailing new user cold start problem by utilizing only movie genres. Unlike other techniques, in the proposed work, familiarity of each movie genre is considered to compute the genre significance value. Based on genre significance value, genre similarity is correlated to render recommendations to a new user. The evaluation of the proposed recommender algorithm on real-world datasets shows that the algorithm performs better than the other similar approaches.

scholarly journals Tag-Aware Personalized Recommendation Using a Hybrid Deep Model

2017 ◽  
Author(s):  
Zhenghua Xu ◽  
Thomas Lukasiewicz ◽  
Cheng Chen ◽  
Yishu Miao ◽  
Xiangwu Meng
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Feature Space ◽  
Personalized Recommendation ◽  
Neural Network Approach ◽  
Deep Model ◽  
Deep Feature ◽  
Training Quality ◽  
Training Efficiency ◽  
Better Than

Recently, many efforts have been put into tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). To ensure the scalability in practice, we further propose to improve this model's training efficiency by using hybrid deep learning and negative sampling. Experimental results show that our approach can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation (3.8 times better than the best baseline), and that using hybrid deep learning and negative sampling can dramatically enhance the model's training efficiency (hundreds of times quicker), while maintaining similar (and sometimes even better) training quality and recommendation performance.

scholarly journals STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

2019 ◽  
Author(s):  
Jiani Zhang ◽  
Xingjian Shi ◽  
Shenglin Zhao ◽  
Irwin King
Keyword(s):  
Recommender Systems ◽  
Matrix Completion ◽  
Model Space ◽  
Cold Start ◽  
Training Strategy ◽  
Convolutional Networks ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Reconstructed Graph ◽  
Node Embeddings

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.

A Novel Overlapping Method to Alleviate the Cold-Start Problem in Recommendation Systems

2021 ◽  
Vol 31 (09) ◽  
pp. 1277-1297
Author(s):  
Ali M. Ahmed Al-Sabaawi ◽  
Hacer Karacan ◽  
Yusuf Erkan Yenice
Keyword(s):  
Social Relations ◽  
Average Distance ◽  
Recommendation Systems ◽  
Cold Start ◽  
Absolute Error ◽  
Sufficient Information ◽  
Partitioning Around Medoids ◽  
Real World Datasets ◽  
Overlapping Method ◽  
Cold Start Problem

Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: “cold-start”; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient information about users or items. Since RSs performance is substantially limited by cold-start users and cold-start items problems; this research study takes the route for a major aim to attenuate users’ cold-start problem. Still in the process of researching, sundry studies have been conducted to tackle this issue by using clustering techniques to group users according to their social relations, their ratings or both. However, a clustering technique disregards a variety of users’ tastes. In this case, the researcher has adopted the overlapping technique as a tool to deal with the clustering technique’s defects. The advantage of the overlapping technique excels over others by allowing users to belong to multi-clusters at the same time according to their behavior in the social network and ratings feedback. On that account, a novel overlapping method is presented and applied. This latter is executed by using the partitioning around medoids (PAM) algorithm to implement the clustering, which is achieved by means of exploiting social relations and confidence values. After acquiring users’ clusters, the average distances are computed in each cluster. Thereafter, a content comparison is made regarding the distances between every user and the computed distances of the clusters. If the comparison result is less than or equal to the average distance of a cluster, a new user is added to this cluster. The singular value decomposition plus (SVD[Formula: see text]) method is then applied to every cluster to compute predictions values. The outcome is calculated by computing the average of mean absolute error (MAE) and root mean square error (RMSE) for every cluster. The model is tested by two real world datasets: Ciao and FilmTrust. Ultimately, findings have exhibited a great deal of insights on how the proposed model outperformed a number of the state-of-the-art studies in terms of prediction accuracy.

MFS-LDA: a multi-feature space tag recommendation model for cold start problem

2017 ◽  
Vol 51 (3) ◽  
pp. 218-234 ◽  
Author(s):  
Muhammad Ali Masood ◽  
Rabeeh Ayaz Abbasi ◽  
Onaiza Maqbool ◽  
Mubashar Mushtaq ◽  
Naif R. Aljohani ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Feature Space ◽  
Cold Start ◽  
Tag Recommendation ◽  
Social Bookmarking ◽  
Content Type ◽  
Feature Spaces ◽  
Multiple Feature ◽  
Cold Start Problem ◽  
Dirichlet Allocation

Purpose Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem. Design/methodology/approach MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem). Findings Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods. Originality/value The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.

Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start Problem

2020 ◽  
pp. 195-216
Author(s):  
Sharon Moses J. ◽  
Dhinesh Babu L. D. ◽  
Santhoshkumar Srinivasan ◽  
Nirmala M.
Keyword(s):  
Genetic Algorithm ◽  
Collaborative Filtering ◽  
Cold Start ◽  
Filtering Algorithm ◽  
Other Information ◽  
Collaborative Filtering Algorithm ◽  
Real World Datasets ◽  
Gender Based ◽  
Recommender Algorithm ◽  
Cold Start Problem

Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. Cold start problem is one of the prevailing issues in recommendation system where the system fails to render recommendation. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of user gender is less explored when compared with other information like age, profession, region, etc. In this chapter, genetic algorithm influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state-of-art approaches.

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