Movie Recommender System

ABSTRACT

Research on recommendation systems has gained a considerable amount of attention

Over the past decade as the number of online users and online contents continue to grow

At an exponential rate. With the evolution of the social web, people generate and consume

Data in real time using online services such as Twitter, Face book, and web news portals. With

The rapidly growing online community, web-based retail systems and social media sites have

To process several millions of user requests per day. Generating quality recommendations

Using this vast amount of data is itself a very challenging task. Nevertheless, opposed to the

Web-based retailers such as Amazon and Netflix, the above-mentioned social networking

Sites have to face an additional challenge when generating recommendations as their contents

Are very rapidly changing. Therefore, providing fresh information in the least amount of

Time is a major objective of such recommender systems. Although collaborative filtering is a

Widely used technique in recommendation systems, generating the recommendation model

Using this approach is a costly task, and often done offline. Hence, it is difficult to use collaborativefiltering in the presence of dynamically changing contents, as such systems require

Frequent updates to the recommendation model to maintain the accuracy and the freshness

Of the recommendations. Parallel processing power of graphic processing units (gpus) can

Be used to process large volumes of data with dynamically changing contents in real time,

And accelerate the recommendation process for social media data streams. In this paper, we

Address the issue of rapidly changing contents, and propose a parallel on-the-fly collaborative

Filtering algorithm using gpus to facilitate frequent updates to the recommendations model.

We use a hybrid similarity calculation method by combining the item–item collaborative filtering

With item category information and temporal information. The experimental results on

real-world datasets show that the proposed algorithm outperformed several existing online

CF algorithms in terms of accuracy, memory consumption, and runtime. It was also observed

that the proposed algorithm scaled well with the data rate and the data volume, and generated

recommendations in a timely manner.

EXISTING SYSTEM

Although collaborative filtering is awidely used technique in recommendation systems, generating the recommendation modelUsing this approach is a costly task, and often done offline. Hence, it is difficult to use collaborative filtering in the presence of dynamically changing contents; as such systems requirefrequent updates to the recommendation model to maintain the accuracy and the freshnessof the recommendations. Parallel processing power of graphic processing units (gpus) canbe used to process large volumes of data with dynamically changing contents in real time,and accelerate the recommendation process for social media data streams.

DIS ADVANTAGES

  • difficult to use collaborative filtering
  • costly task
  • systems require Frequent updates

PROPOSED SYSTEM

In this paper we proposea novel GPU-accelerated, item-based recommender system for social media streams, usinga hybrid similarity function computed based on conditional probability, item category information,and temporal information. Experiment results on real-world datasets show that ourproposed method outperforms two existing onlineCF algorithms [30, 37] in terms of accuracy,memory usage, and runtime.

ADVANTAGES:

• Propose a novel item-based collaborative filtering algorithm to support dynamically

Changing contents in continuous data streams.

• Use GPUs to accelerate item-based CF process and rapidly update the recommendation

Model in the presence of high input rates and large volumes of streaming data.

• Through extensive experiments, we demonstrate the effectiveness and efficiency of our

Proposed algorithm in terms of accuracy, memory usage, runtime, and scalability.

SYSTEM REQUIREMENTS

H/W System Configuration:-

Processor - Pentium –III

RAM - 256 MB (min)

Hard Disk - 20 GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA

S/W System Configuration:-

Operating System : Windows95/98/2000/XP

Application Server : Tomcat5.0/6.X

Front End : HTML, Jsp

Scripts : JavaScript.

Server side Script : Java Server Pages.

Database : MySQL 5.0

Database Connectivity : JDBC

Further Details Contact: A Vinay 9030333433, 08772261612, 9014123891

#301, 303 & 304, 3rd Floor, AVR Buildings, Opp to SV Music College, Balaji Colony, Tirupati - 515702

Email: |