Traditional POI recommendation systems use a centralized data storage approach to train models, posing significant risks of privacy breaches. Federated learning offers an effective solution to ...address user privacy concerns. However, in existing federated learning setups, client data remains isolated from each other, making it challenging to achieve cross-client collaborative training and severely limiting the performance of POI models. Additionally, the sparsity of local client data makes it difficult for local models to effectively learn local personalized knowledge, and low-quality local models further degrade the performance of the global model. Furthermore, the potential of semantic information in representing deep user behavior characteristics hasn't been fully explored in federated POI recommendation. Therefore, this paper proposes a semantic-based federated learning method (SFL), introducing edge devices to facilitate cross-client personalized knowledge collaboration. We design a semantic-based collaborative optimization strategy to learn and utilize semantic information from client trajectories without sensitive data, guiding edge devices to mine shared user knowledge for achieving knowledge collaboration among similar clients. Simultaneously, the semantic information from client trajectories is utilized to enhance local data, thereby improving the personalized capabilities of local models. Extensive experiments on public datasets demonstrate that SFL outperforms several strong baselines in terms of performance.
With increment in the utilization of Internet, the pace of increment of social networks is getting ubiquitous in recent years. This paper focuses on the job portal websites. The research objective of ...this paper is that the recommender framework takes the abilities from the website and makes suggestion to the candidates with the jobs whose descriptions are coordinating with their profiles the most. This paper additionally presents a short presentation on recommender framework and talks about different categories of this framework. From the start, information is cleaned by expelling the filthy information as extra space and duplicates. Then the job recommendations are made to the target applicants on the basis of their preferences. It utilizes different Machine Learning procedures which results show that Random Forest Classifier (RFC) gives the most noteworthy expectation accuracy when contrasted with different procedures. Finally, the optimization technique is utilized to get the most exact outcome. The advantage of recommender framework in career orientation is expressed. Geo-area based recommendation framework is utilized to find the organization's position which can assist the ideal applicants with reaching their destination. This examination shows that the utilization of job recommender system can assist with improving the recommendation of appropriate employment for work searchers.
With the development of location-based services (LBS), many location-based social sites like Foursquare and Plancast have emerged. People can organize and participate in group activities on those ...sites. Therefore, recommending venues for group activities is of practical value. However, the group decision making process is complicated, requiring trade-offs among group members. And the data sparsity and cold-start problems make it difficult to make effective group recommendation. In this manuscript, we propose a Multi-view Group Representation Learning (MGPL) framework for location-aware group recommendation. The proposed multi-view group representation learning framework can leverage multiple types of information for deep representation learning of group preferences and incorporate the spatial attributes of locations to further capture the group mobility preferences. Experiments on two real datasets Foursqaure and Plancast show that our method significantly outperforms the-state-of-art approaches.
•A method for cross-context interpretations of health and wellness recommendations.•A mechanism of refining generalized recommendations to personalized recommendations.•The contextual interpretations ...are made for increasing the user acceptability of a system.
A huge array of personalized healthcare and wellness systems are introduced into the portfolio of digital health and quantified-self movement in recent years. These systems share common capabilities including self-tracking/monitoring and self-quantifications, based on the raw sensory data. These capabilities provide solid ground for the users to be more aware of their health; however, such measures are inefficient for changing the unhealthy habits of the users. In order to induce healthy habits in the users, a system must be capable of generating context-aware personalized recommendations. The main obstacle in this regard is the contextual interpretation of recommendations based on user's current context and contextual preferences. To resolve these issues, we propose a methodology of cross-context interpretation of recommendations (CCIR) for personalized health and wellness services. The CCIR method adds additional capabilities to the traditional reasoning methods and builds advanced form of the reasoning with the incorporation of contextual factors in the process of interpretations of the recommendations. With CCIR, the self-quantification systems can be enhanced to generate personalized recommendations in addition to tracking, quantifying, and monitoring user activities. In order to validate the proposed CCIR methodology, a set of 40 contextual scenarios and corresponding recommendations are presented for the evaluation collected from 40 different end users and 10 domain experts. Using chi-square test evaluation, the results demonstrated acceptable “goodness of fit” indices for the system developed on proposed CCIR methodology with respect to the end users’ opinion. Also from the statistical observation, it is found that there exists a higher level agreement towards the system between the participants of both end users and experts.
Organizing Books and Authors by Multilayer SOM Haijun Zhang; Chow, Tommy W. S.; Wu, Q. M. Jonathan
IEEE transaction on neural networks and learning systems,
12/2016, Letnik:
27, Številka:
12
Journal Article
This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich ...tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization.
Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the ...recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for
session-based recommendation algorithms
that typically aim to predict the user’s immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like
gru4rec
, factorized Markov model approaches such as
fism
or
fossil
, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today’s more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms.
In the absence of user profile information, recommender systems have to only rely on current session information for recommendation. E-commerce sites may use transitions between interactions in each ...session to improve recommendation. This situation is known as the session-based recommendation. It can be challenging due to the limited information and the uncertain user behavior. Recurrent Neural Networks (RNN) have become the state-of-the-art models for session-based recommendation due to their ability to model long sequences. Although powerful, RNN-based models suffer from learning complex transition between the interactions. To mitigate it, Graph Neural Networks (GNN) have been proposed for session-based recommendation. However, different sequences of interactions may lead to the same outcome especially on E-commerce sites, hence non-sequential interactions between items of the current session may improve the performance of a recommender system. To learn both the sequential and non-sequential transition interactions between the items in the current session, we proposed a GNN based model named GRASER. Specifically, the proposed model first learns the non-sequential and then the sequential transition interactions between the items of the current session using GNN in an end-to-end manner. Extensive experiments were carried out on two datasets: Yoochoose from the RecSys Challenge 2015 and Diginetica from CIKM Cup 2016. The results showed that the proposed model outperforms the other state-of-the-art models by 11% and 10% on MRR@20 on Yoochoose and Diginetica datasets respectively.
As the application scenarios of recommendation algorithms are becoming increasingly complex, the efficiency of traditional recommendation algorithm based on accuracy is no longer satisfied. To solve ...this problem, an improved matrix factorization based model for many-objective optimization recommendation is proposed to simultaneously optimize the four recommendation objectives of novelty, diversity, accuracy, and recall. As a novel double-layer recommendation model, two improved algorithms are composed: 1) For the bottom layer, an improved matrix factorization algorithm with additional regularization constraints is used to predict unknown item ratings; 2) For the top layer, the recommendation list is optimized by a many-objective evolutionary algorithm. Comprehensive experiments demonstrate that the proposed model can effectively improve the four recommended evaluation metrics. And a recommended list with novel and diverse items is provided for users in a more efficient way while maintaining accuracy.
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