1. Mashup Service Recommendation based on User Interest and Service Network
Buqing Cao
ICWS2013, IJWSR

2. 16:16
Introduction(1)
With the emergence of Web2.0 and its related technologies, mashups, which are Web applications created by combining two or more services, are becoming a hot research topic. Mashup technology has many advantages (such as easier programming, shorter development time, higher quality plans, and more interested outputs, which make it becomes increasingly popular. For example, to June 2013, Programmableweb.com has published more than 7099 mashup services. Moreover, several mashup tools have been developed, such as Microsoft Popfly, Google Mashup Editor and IBM Mashup Center. Typical mashup applications include Map, Video and Image, Search and Shopping, News, Microblog Mashup, etc.
Mashup Service Recommendation based on User Interest and Social Network B Cao, J Liu, M Tang, Z Zheng, G Wang

3. Search,Shopping mashup
16:16
Introduction(2)
Map mashup
Video,Photo mashup
Search,Shopping mashup
Moreover, several mashup tools have been developed, such as Microsoft Popfly, Google Mashup Editor and IBM Mashup Center. Typical mashup applications include Map, Video and Image, Search and Shopping, News, Microblog Mashup, etc.
Mashup Service Recommendation based on User Interest and Social Network B Cao, J Liu, M Tang, Z Zheng, G Wang

4. Introduction(3) Research issue Idea
16:16
Introduction(3)
Research issue
With the rapid growth in the number of available mashup services, how to recommend suitable Mashup services to users is a challenging problem.
Idea
focus on Mashup service recommendation which is seldom addressed by previous work. (Most existing works focus on Web service recommendation.)
propose a novel Mashup service recommendation approach which takes both users’ personal interest and relationship between Mashups into consideration. This method can achieve a good balance between recommendation accuracy and diversity.
conduct a set of experiments on a real-world mashup dataset to evaluate the performance of our recommendation approach.
A prototype system is developed to implement our Mashup service recommendation approach.
Mashup Service Recommendation based on User Interest and Social Network B Cao, J Liu, M Tang, Z Zheng, G Wang

5. Framework of Our Approach
16:16
Framework of Our Approach
Mashup Service Recommendation based on User Interest and Social Network B Cao, J Liu, M Tang, Z Zheng, G Wang

6. Prototype Implementation (1)
16:16
Prototype Implementation (1)
A Mashup service recommendation prototype system is developed to implement the proposed method.
The Mashup service recommendation prototype system has functionalities of Mashup service crawling and registration, Mashup service search and management, TextSimilarity and SRSimilarity computing, Mashup service recommendation, and so on. Its framework is as shown in Figure 6.
Crawl and Registration of Mashup Services, Web APIs and Tags
ProgrammableWeb.com is the leading directory of mashup services applications and open Web APIs, has published 7580 Web APIs and 6798 mashup services as of October 2012.We crawl and register these mashup services, Web APIs and tags from programmableweb.com as the origin data, and then store them to local database using the forms as Mashup<Id, Name, APIs, tags …>, API<Id, Name, tags…>, and Tag<Id, Name, tags …>.
User History on Mashup Services
After a user has searched and used some mashup services of the system recently, we will preprocess, save and integrate these historical, used mashup services documents which are consist of mashup service name, description, their Web APIs and tags, and their developer information, and so on.
User Interest Model
By the user history on mashup services, a user interest vector will be built by the system. Meanwhile, a preprocessing will be performed for each mashup service document in this system and then form a mashup service vector. Finally, the system will calculate the text similarity between user interest vector and mashup service vector, and finish the construction of user interest model.
Service Network Model
The system extracts the social calling relationship between mashup services and Web APIs, and the social marking relationship between mashup services and tags, or Web APIs and tags. By integrating these two relationships, the system computes the social relationship similarity among mashup services, and then generates mashup service network.
Mashup Service Recommendation
Based on user interest model and social relationship network model, the system will realize mashup service recommendation. When a user searches a mashup service, the system will perform the total similarity computing, and then execute Top-K mashup service recommendation algorithm, and finally can find the top-K similar mashup services or mashup services composition paths with the total similarity more than threshold .
Mashup Service Recommendation based on User Interest and Social Network B Cao, J Liu, M Tang, Z Zheng, G Wang

7. Prototype Implementation (2/2)
16:16
Prototype Implementation (2/2)
Mashup Service Recommendation based on User Interest and Social Network B Cao, J Liu, M Tang, Z Zheng, G Wang