4,887 research outputs found
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
The analysis and mining of user heterogeneous behavior are of paramount
importance in recommendation systems. However, the conventional approach of
incorporating various types of heterogeneous behavior into recommendation
models leads to feature sparsity and knowledge fragmentation issues. To address
this challenge, we propose a novel approach for personalized recommendation via
Large Language Model (LLM), by extracting and fusing heterogeneous knowledge
from user heterogeneous behavior information. In addition, by combining
heterogeneous knowledge and recommendation tasks, instruction tuning is
performed on LLM for personalized recommendations. The experimental results
demonstrate that our method can effectively integrate user heterogeneous
behavior and significantly improve recommendation performance.Comment: Accepted at RecSys 202
Improving the Performance of Recommendation on Social Network by Investigating Interactions of Trust and Interest Similarity
On the social media, lots of people share their experiences through various factors like blogs, online ratings, reviews, online polling and tweets. Study shows that the factors such as interpersonal interest and interpersonal influence from the social media which is based on the circles as well as groups of friends leads to opportunities and challenges in solving the problems on datasets. This challenge is for the Recommender System (RS) to find the solution on cold start and sparsity problems. In this paper, on the basis of the probabilistic matrix factorization, the social factors like personal interest, interpersonal influence and interpersonal interest similarity are combined into a unified personalized recommendation model. These factors can improve the associating linkage in latent space. Various datasets are used to conduct the experiments to get the results that show that the proposed model performs better than the existing approaches
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Hybrid Temporal Dynamics Feature Extraction in Recommendation Systems for Improved Ranking of Items
In today's retail landscape, shopping malls and e-commerce platforms employ various psychological tactics to influence customer behavior and increase profits. In line with these strategies, this paper introduces an innovative method for recognizing sentiment patterns, with a specific emphasis on the evolving temporal aspects of user interests within Recommendation Systems (RS). The projected method, called Temporal Dynamic Features based User Sentiment Pattern for Recommendation System (TDF-USPRS), aims to enhance the performance of RS by leveraging sentiment trends derived from a user's past preferences. TDF-USPRS utilizes a hybrid model combining Short Time Fourier Transform (STFT) and a layered architecture based on Bidirectional Long Short-Term Memory (BiLSTM) to retrieve temporal dynamics and discern a user's sentiment trend. Through an examination of a user's sequential history of item preferences, TDF-USPRS produces sentiment patterns to offer exceptionally pertinent recommendations, even in cases of sparse datasets. A variety of popular datasets, including as MovieLens, Amazon Rating Beauty, YOOCHOOSE, and CiaoDVD are utilised to assess the suggested technique. The TDF-USPRS model outperforms existing approaches, according to experimental data, resulting in recommendations with greater accuracy and relevance. Comparing the projected model to existing approaches, the projected model displays a 6.5% reduction in RMSE and a 4.5% gain in precision. Specifically, the model achieves an RMSE of 0.7623 and 0.996 on the MovieLens and CiaoDVD datasets, while attaining a precision score of 0.5963 and 0.165 on the YOOCHOOSE and Amazon datasets, respectively
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Extracting Interests of Users from Web Log Data Log
The knowledge on the cobweb is growing expressively. Without a recommendation theory, the clients may come through lots of instance on the network in finding the knowledge they are stimulated in. Today, many web recommendation theories cannot give clients adequate symbolized help but provide the client with lots of immaterial knowledge. One of the main reasons is that it can't accurately extract users interests. Therefore, analyzing users' Web Log Data and extracting users' potential interested domains become very important and challenging research topics of web usage mining. If users' interests can be automatically detected from users' Web Log Data, they can be used for information recommendation and marketing which are useful for both users and Web site developers. In this paper, some novel algorithms are proposed to mine users' interests. The algorithms are based on visit time and visit density which can be obtained from an analysis of web users' Web Log Data. The experimental results of the proposed methods succeed in finding users interested domains
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation
The successful integration of large language models (LLMs) into
recommendation systems has proven to be a major breakthrough in recent studies,
paving the way for more generic and transferable recommendations. However, LLMs
struggle to effectively utilize user and item IDs, which are crucial
identifiers for successful recommendations. This is mainly due to their
distinct representation in a semantic space that is different from the natural
language (NL) typically used to train LLMs. To tackle such issue, we introduce
ControlRec, an innovative Contrastive prompt learning framework for
Recommendation systems. ControlRec treats user IDs and NL as heterogeneous
features and encodes them individually. To promote greater alignment and
integration between them in the semantic space, we have devised two auxiliary
contrastive objectives: (1) Heterogeneous Feature Matching (HFM) aligning item
description with the corresponding ID or user's next preferred ID based on
their interaction sequence, and (2) Instruction Contrastive Learning (ICL)
effectively merging these two crucial data sources by contrasting probability
distributions of output sequences generated by diverse tasks. Experimental
results on four public real-world datasets demonstrate the effectiveness of the
proposed method on improving model performance.Comment: 11 pages, 7 figure
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