Collaborative Filtering Service Recommendationbased on a Novel Similarity Computation Method

Collaborative Filtering Service RecommendationBased on a Novel Similarity Computation Method

ABSTRACT:

Recently, collaborative filtering-based methods are widely used for service recommendation. QoS attribute valuebasedcollaborative filtering service recommendation includes two important steps. One is the similarity computation, and theother is the prediction for the QoS attribute value, which the user has not experienced. In some previous studies, the similaritycomputation methods and prediction methods are not accurate. The performances of some methods need to be improved. Inthis paper, we propose a ratio-based method to calculate the similarity. We can get the similarity between users or betweenitems by comparing the attribute values directly. Based on our similarity computation method, we propose a new method topredict the unknown value. By comparing the values of a similar service and the current service that are invoked by commonusers, we can obtain the final prediction result. The performance of the proposed method is evaluated through a large dataset of real web services. Experimental results show that our method obtains better prediction precision, lower mean absoluteerror (MAE) and faster computation time than various reference schemes considered.

EXISTING SYSTEM:

Presently, the Pearson correlation coefficient (PCC) and cosine (COS) methods are commonly applied to calculate the similarity.

DISADVANTAGES OF EXISTING SYSTEM:

Pearson correlation coefficient (PCC) and cosine (COS)methods have limited accuracy.

PCC method doesnot take the differences of QoS attributes values given bydifferent users into account. Although the COS method can measure the angles of the vectors, which are composed by the users or services, it neglects the lengths of the vectors.

PROPOSED SYSTEM:

In this paper, we propose a new method to calculate thesimilarity.

Generally, the QoS attributes experienced by the user aregiven in the form of numerical values, and these values arenon-negative. The similarity represents the degree of twoobjects’ consistency. We can use the ratio of two values toexpress the consistency.

The ratio of two attribute valueswhich is the results of two users invoking the same itemreflects the users’ consistency on this item, i.e., the singlesimilarity. Summing up all the single similarities togetherand getting the average, we can obtain the final similaritybetween two users.

ADVANTAGES OF PROPOSED SYSTEM:

Our method is applicable toall kinds of QoS attributes which are given in the numericalvalues. However, some of the qualitative and subjectiveQoS attributes are expressed in non-numerical value, suchas “very good”, “good”, and so on. According to certainrules, these evaluations can be transformed into numericalvalues, and then our method can be used.

The recommendation system can recommend appropriate service(s) to the user according to given conditions. Here, the specific condition given by a user may be constrained by multiple objectives

Save a lot of time and energy

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System: Pentium Dual Core.

Hard Disk : 120 GB.

Monitor: 15’’ LED

Input Devices: Keyboard, Mouse

Ram:1 GB

SOFTWARE REQUIREMENTS:

Operating system : Windows 7.

Coding Language:JAVA/J2EE

Tool:Netbeans 7.2.1

Database:MYSQL

REFERENCE:

Xiaokun Wu, Bo Cheng, and Junliang Chen, “Collaborative Filtering Service RecommendationBased on a Novel Similarity Computation Method”,IEEE TRANSACTIONS ON SERVICE COMPUTING, VOL.10, NO.3, May-June 2017.