International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
A Mechanism for Handling Cold Start in Book
Recommender System by Sharing Student
Profile.
Umar Bashir Mir1 Mubbashir Nazir2 and Anuj Yadav3
1,2
3
M.Tech Scholar Department of Computer Science & Information Technology DIT University Dehradun
Assistant Professor Department of Computer Science & Information Technology DIT University Dehradun
Abstract
In Recommendation system, one of the main concerns is the insufficient amount of information available about the new users or
new items. In literature this problem is termed as Cold Start, which affects the actionability of recommendation results. In this
paper we have devised a methodology for handling cold start problem in Recommendation System (CF). The Proposed approach
provides solution to this problem by sharing user profile.
Keywords: Cold start, profile sharing, collaborative filtering, recommender systems.
1. INTRODUCTION
Recommender systems are software tools which represent user preferences for the purpose of suggesting items to purchase
or examine. Recommender system play a vital role in ecommerce where recommendation is made by processing huge
information in order to suggest items which best meet user needs and preferences.
Many techniques have been proposed for providing recommendations based on techniques such as Collaborative Filtering
(CF), Content Based (CB) etc. Among them CF technique is considered one of the most successful recommendation
technique. This technique allows recommending an item to a user based on the user profiles that are closest to him [9, 28,
29].Although CF has been used extensively; it also has some disadvantages, such as the cold-start problem. Cold start
problem occurs when insufficient rating data is available in the initial state. The recommender system lacks data to
produce appropriate recommendations. It occurs because of the presence of new user or new item. New User Problem:
When recommendations follow from user-to-user correlations based on the accumulation of ratings, a user with few
ratings is difficult to categorize. New Item Problem: An item with few ratings cannot easily be recommended. This
problem occurs particularly in domains with many new items (e.g. news articles) [10].
2. Related Work
Collaborative filtering (CF) systems use community ratings to determine recommendations for users. CF systems typically
work by associating a user with a group of likeminded users, called neighbourhoods, then recommending items enjoyed
by others in the group. The movie domain is probably the most widely studied application area for CF systems [9, 28, 29,
25, 17, 32], though the same methodology is applicable across a number of domains, including books, music [30], web
pages [6], jokes [8], news, newsgroups [27, 14, 19], advertisements, and more. CF algorithms range from simple nearestneighbour methods [6, 27, 29] (Breese et al. [6] call these “memory-based” methods) to more elaborate model-based
methods which first train or “compile” a model for example, a Bayesian network [6], a classifier [5, 20], a co-clustered
matrix [31], or a truncated singular value decomposition matrix [5, 8,28]—based on historical data, then use the trained
model to generate recommendations. Hundreds of CF algorithms have been proposed and studied, including machine
learning methods [5, 4, 15, 26], graph-based methods [1, 12], linear algebra based methods [5, 8, 28], and probabilistic
methods [11, 23, 24]. A number of hybrid methods combining information filtering (IF) and CF techniques have also
been proposed [3, 2, 4, 24, 18, 9], which are especially useful when data is sparse, for example in cold-start situations. In
fact, in the extreme cold-start setting, CF methods cannot provide recommendations at all, and IF methods or hybrid
IF/CF methods are needed. We base our methodology on sharing users (in our case student) profile in a controlled
manner for generating recommendations. This shared profile is then treated as a separate user in a CF system.
3. THE PROPOSED APPROACH
The Proposed approach handles cold-start problem in recommendation systems (Book Recommendation System) by
formulating User Profiles (students in our case) taking into consideration the various attributes of users such as class,
stream, preferred authors (local or international) etc. The profile template will be common to all students.
3.1 Methodology
Using appropriate methodologies is as important as identifying the right recommendations. The Proposed approach is
formed of three main phases: Fetch, Process and Truncate. The Fetch phase is concerned with getting the required
Volume 2, Issue 12, December 2013
Page 318
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
parameters such as Global Student Profile or Shared Profile (Profile Token) for Process Phase. Process is the main engine
where the actual recommendations are generated based on inputs from the Fetch phase. Truncate is involved with
discarding of the profile-tokens used during the Fetch phase.
Algorithm for Sharing Profile
Fetch Phase
Stage 1: Use Global student profile (say GSP) or Shared profile (Profile Token).
Stage 2: If Profile Token is chosen from Stage 1 then enter Profile-Token.
Process Phase
Stage 3: Clone selected profile (whichever is selected from Fetch phase) as students personalized profile.
Stage 4: Generate initial recommendations using cloned profile (students personalized profile from stage 3).
Stage 5: Analyse student’s response to these recommendations and continuously update personalized profile for this
student.
Truncate Phase
Stage 6: Discard Profile-token.
Fig 1: General Architecture of Profile Sharing Mechanism in www.xbooksindia.com
Since attributes of the students are bound to change but the only difference is that some will be changing frequently like
subjects, price sensitivity etc. and other will be changing less frequent like class stream etc. taking into consideration the
various factors such as Semester timing, time-to-exams etc. the process of determining the price sensitivity should be
based on the data captured on-line(Real Time Data) as well as off-line(Stored Data).
3.2 Algorithm for Price Sensitivity
Stage 1: Identify unique students .
Volume 2, Issue 12, December 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
Stage 2:
Stage 3:
Stage 4:
Stage 5:
Stage 6:
Store a list L of books displayed to the student.
Store the price for each book in list L.
Arrange books in list L according to their categories (like programming, Networking etc)
Calculate Pmin (minimum book price) and Pmax (maximum book price) for each category in the list L
For each book clicked by the user calculate the normalized book price as
where PPn is the normalized book price , Pp is the clicked book price, Pmin is the minimum book price in the list L and
Pmax is the maximum book price in the list
Stage 7: Calculate the price sensitivity of each student, as For a new user
where PSu is the price sensitivity of a student, NCu is the total no of products clicked by the student, PPn
normalized book price.
is the
For a returning user
where PSp is the previous price sensitivity of a returning student.
Stage 8: Store the Price sensitivity of each student.
Figure 2 Price Sensitivity Score (tested on dummy data).
Figure 3 Segment Price Sensitivity (tested on dummy data)
Figure 4 Price Sensitivity score (tested on dummy data)
Volume 2, Issue 12, December 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
Fig 4: Flow Chart depicting holistic flow in the system
4. Conclusion and future work
Thus the research work presented will handle cold-start problem by sharing students profile in a controlled manner
(without personal details like age, email etc.). The proposed work is implemented in an ecommerce website xbooksindia
(www.xbooksindia.com) which is currently in its final phase. Also price sensitivity module has been successfully tested in
xbooksindia (www.xbooksindia.com) website where it was observed that the systems overall performance was improved
by good margin.
This mechanism is specifically focused on students and books, with some modifications it can be extended to other
domains as well.
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Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
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