Relational collaborative topic regression for recommender systems

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Relational collaborative topic regression for recommender systems +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Relational Collaborative Topic Regression for Recommender Systems
Abstract:
Due to its successful application in recommender systems, collaborative
filtering (CF) has become a hot research topic in data mining and
information retrieval. In traditional CF methods, only the feedback matrix,
which contains either explicit feedback (also called ratings) or implicit
feedback on the items given by users, is used for training and prediction.
Typically, the feedback matrix is sparse, which means that most users
interact with few items. Due to this sparsity problem, traditional CF with
only feedback information will suffer from unsatisfactory performance.
Recently, many researchers have proposed to utilize auxiliary information,
such as item content (attributes), to alleviate the data sparsity problem in
CF. Collaborative topic regression (CTR) is one of these methods which has
achieved promising performance by successfully integrating both feedback
information and item content information. In many real applications,
besides the feedback and item content information, there may exist
relations (also known as networks) among the items which can be helpful
for recommendation. In this paper, we develop a novel hierarchical
Bayesian model called Relational Collaborative Topic Regression (RCTR),
which extends CTR by seamlessly integrating the user-item feedback
information, item content information, and network structure among items

into the same model. Experiments on real-world datasets show that our
model can achieve better prediction accuracy than the state-of-the-art
methods with lower empirical training time. Moreover, RCTR can learn
good interpretable latent structures which are useful for recommendation.
Existing System:
In most traditional CF methods, only the feedback matrix, which contains
either explicit feedback (also called ratings) or implicit feedback on the
items given by users, is used for training and prediction. Typically, the
feedback matrix is sparse, which means that most items are given feedback
by few users or most users only give feedback to few items. Due to this
sparsity problem, traditional CF with only feedback information will suffer
from unsatisfactory performance.
Proposed System:
We develop a novel hierarchical Bayesian model, called Relational
Collaborative Topic Regression (RCTR), to incorporate item relations for
recommendation.
By extending CTR, RCTR seamlessly integrates the user-item feedback
information, item content information and relational (network) structure
among items into a principled hierarchical Bayesian model.
Even if a new item has been given feedback only by one or two users,
RCTR can make effective use of the information from the item network to
alleviate the data sparsity problem in CF, which will consequently improve
the recommendation accuracy.

Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• RAM : 256 Mb.
Software Requirements:
• Operating system : - Windows XP.
• Front End : - JSP
• Back End : - SQL Server
Software Requirements:
• Operating system : - Windows XP.
• Front End : - .Net
• Back End : - SQL Server

This document summarizes a study that proposes a neural network learning model for recommender systems. The study aims to improve collaborative filtering methods by estimating user preferences based on learned correlations between users through a neural network. The proposed method was tested on MovieLens data and showed improved precision of 6.7% compared to other techniques. Additionally, the study found that precision and recall improved further, by 3.5% and 2.4% respectively, when including film genre information in the neural network learning. The document concludes the proposed technique can utilize diverse data sources and perform well regardless of data complexity compared to other recommender system methods.

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This document describes a hybrid recommendation system used at LinkedIn that generates "virtual profiles" to address cold start problems. Virtual profiles augment item profiles by inheriting rich features from members who have shown interest in that item. The system extracts features from user profiles to generate primary profiles for items, and then generates virtual profiles for items by selecting top features from affiliated user profiles using mutual information. An experiment on LinkedIn's community recommendation problem found virtual profiles outperformed collaborative filtering and improved recommendations for new users.

Recsys virtual-profiles

Haishan Liu

This document describes a hybrid recommendation system used at LinkedIn that generates "virtual profiles" to address cold start problems. Virtual profiles augment item profiles by inheriting rich features from members who have shown interest in that item. The system uses a content-based filtering approach, extracting features from user and item profiles to create indexes and queries. It generates virtual profiles for items by selecting the most informative features from interested user profiles based on mutual information. An experiment on LinkedIn community recommendations found virtual profiles outperformed collaborative filtering, especially for new users.

Dynamic personalized recommendation on sparse data

JPINFOTECH JAYAPRAKASH

The document proposes a novel dynamic personalized recommendation algorithm that utilizes information from both user ratings and profiles to provide high-quality recommendations on sparse data. It explores latent relationships between ratings and user profiles and designs dynamic features to describe changing user preferences over time. An adaptive weighting approach is used to combine the dynamic features for personalized recommendations, taking into account time and data density to adapt to dynamic recommendations on sparse data. Experimental results on public datasets show the algorithm has satisfactory performance.

Multidirectional Product Support System for Decision Making In Textile Indust...

IOSR Journals

This document proposes a multidirectional rank prediction algorithm (MDRP) for decision making in the textile industry using collaborative filtering methods. MDRP learns asymmetric similarities between users, items, ratings, and sellers simultaneously through matrix factorization to overcome data sparsity and scalability issues. The algorithm was tested on textile datasets and analyzed product and user preferences. Results showed MDRP provided more accurate recommendations than existing similarity learning and collaborative filtering methods. MDRP allows effective decision making for multiple entities with multiple attributes.

ANALYSIS OF ENTERPRISE SHARED RESOURCE INVOCATION SCHEME BASED ON HADOOP AND R

ijaia

The response rate and performance indicators of enterprise resource calls have become an important part of measuring the difference in enterprise user experience. An efficient corporate shared resource calling system can significantly improve the office efficiency of corporate users and significantly improve the fluency of corporate users' resource calling. Hadoop has powerful data integration and analysis capabilities in resource extraction, while R has excellent statistical capabilities and resource personalized decomposition and display capabilities in data calling. This article will propose an integration plan for enterprise shared resource invocation based on Hadoop and R to further improve the efficiency of enterprise users' shared resource utilization, improve the efficiency of system operation, and bring enterprise users a higher level of user experience. First, we use Hadoop to extract the corporate shared resources required by corporate users from the nearby resource storage computer room and terminal equipment to increase the call rate, and use the R function attribute to convert the user’s search results into linear correlations, according to the correlation The strong and weak principles are displayed in order to improve the corresponding speed and experience. This article proposes feasible solutions to the shortcomings in the current enterprise shared resource invocation. We can use public data sets to perform personalized regression analysis on user needs, and optimize and integrate most relevant information.

Analysis of Enterprise Shared Resource Invocation Scheme based on Hadoop and R

gerogepatton

IRJET- E-commerce Recommendation System

IRJET Journal

This document proposes a new advanced e-commerce recommendation system that incorporates additional data beyond a user's purchase history to provide improved recommendations. Specifically, it suggests combining a user's search and purchase history with data from social media platforms like Twitter, reviews from sites like Amazon and Snapdeal, the user's location and preferences. This additional data could allow the system to recommend items a user has not previously purchased. It also describes some limitations of existing recommendation systems that only consider purchase history or require clustering users. The proposed system would use clustering and collaborative filtering together to group similar products and then make recommendations based on the evaluations within each cluster to improve accuracy.

Spe165 t

Rajesh War

This document discusses techniques for detecting duplicate records from multiple web databases. It begins with an abstract describing an unsupervised approach that uses classifiers like the weighted component similarity summing classifier and support vector machine along with a Gaussian mixture model to iteratively identify duplicate records. The document then provides details on related work, including probabilistic matching models, supervised and unsupervised learning techniques, distance-based techniques, rule-based approaches, and methods for improving efficiency like blocking and the sorted neighborhood approach.

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