A NOVEL APROACH TO FEATURE
BASED RECOMMENDATION SYSTEM
BASED ON USER RATINGS
SHAZIA AFREEN
Computer Science and Engineering Department, Amity University, Gomti Nagar, Lucknow, Uttar Pradesh 226028, India
Dr. DEEPAK ARORA
Computer Science and Engineering Department, Amity University, Gomti Nagar Lucknow, Uttar Pradesh 226028, India
POOJA KHANNA
Computer Science and Engineering Department, Amity University, Gomti Nagar, Lucknow, Uttar Pradesh 226028, India
Abstract: With the increase in online shoppers, the demand for suggesting users with appropriate items in lesser time has also increased. The objective of this paper is to propose and devise a method that can take over the current techniques in the aspects of both time and complexity by filtering large databases on the basis of the intended features. The proposed recommendation technique groups the entire user based product dataset into clusters of similar ratings by using Clustering techniques. Each cluster formed can be further classified into Feature based Classes on the basis of the features of the products. The classes under each cluster gives items that are similar to each other on the basis of the feature considered. The proposed concept integrates clustering with classification thereby helping in refining the recommendation process. The results generated thereafter are expected to produce much more personalized, faster and accurate recommendations.
Keywords: Clustering; Feature Extraction; Classification. 1. Introduction
recommendation generation algorithms given by Sarwar was reviewed [Sarwar and et al. (2004)]. A survey on computing item based similarity approaches with time of execution and complexities was done. This paper concentrated upon the advantages and disadvantages of each similarity measure. The conclusion included, that item based algorithm in collaborative filtering provides much faster execution time as well as better quality than the user based collaborative Filtering Approach.
2. Background Study
The paper presents the idea of Collaborative filtering approach of finding users that are similar to each other. The collaborative filtering approach is further classified into user based and item based approach. The next section of paper further emphasizes on item based collaborative filtering approach with the help of diagrams and flow charts. The paper further expresses various similarity measures that are used in existence along with the proposed methodology of a Feature based Recommendation System.
2.1 Collaborative Filtering
The concept of Collaborative Filtering is based on maintaining a database of users along with the items rated by each of them. The purpose of this is to find the users that strongly match with the user under consideration, thereby recommending the items that were strongly rated by the similar users. This concept is used by almost all the existing recommendation systems in the commercial applications like Amazon. Collaborative filtering needs both the user data and the item data along with the ratings. According to the Fig. 1, if User A has rated a Product with the Rating R, and another User B, has rated the same product with same rating R, then it can be said that User A and User B strongly correlate to each other. Collaborative Filtering can be broadly classified into two classes:
1. User based Collaborative Filtering 2. Item based Collaborative Filtering
Fig. 1. The collaborative filtering process
2.1.1 User based Collaborative Filtering
This approach aims at finding recommendations, by matching users and finding the most similar users in terms of preferences and tastes. This method is almost similar to item based collaborative filtering. A similarity method based on correlation is used to find similar users by computing the similarity index between the users. This method is lesser preferred as it consumes much more time than the item based approach. This method works well when the data is not so large, but as the data is scaled to a larger number, the efficiency of this method reduces. The fig. 2. depicts the process of finding the users similar to a particular user U. In this, the products rated by user U is found out. Next the a list containing the users who have rate the same product is created, the users in the list is then compared with the user U, on a certain similarity criteria, the most similar users are then recommended depending upon similarity index used. 2.1.2 Item based Collaborative Filtering
The pseudo code for item to item collaborative filtering is explained as follows: STEP 1: for every item I in the ITEM list:
Find the customers C from the CUSTOMER list, who has rated the Item I STEP 2: for each of the customer C, who has rated the Item I:
Find the Items rated by Customer C, excluding the Item I STEP 3: for each of the Items I’ that have rated by C, Compare I and I’ using some similarity metric
Step 4: Return the Item I’ depending upon the similarity metric.
Fig. 2. User based collaborative filtering process Fig. 3. Item based collaborative filtering process
2.2 Existing Similarity Measures
A similarity method is a way by which the degree of similarity or correlation can be calculated and measured. The existing recommendation systems use many similarity methods. In this paper, the following similarity methods will be touched upon.
1. Cosine Item based Similarity 2. Pearson Item based Similarity 3. Euclidean distance based similarity 4. Tanimoto coefficient based similarity 2.2.1 Cosine based Similarity (COS)
This is a common way of measuring similarity between two customers say, c1 and c2. In this method the two users are expressed in form of vectors, containing the ratings of each product. In the cosine based similarity method, the cosine of the angle between the two vectors c1 and c2 is calculated. The lesser the angle between them, the more is the similarity. The formula that is used to compute the angle between two vectors is depicted by equation (1). Cosine Similarity is easy and efficient to evaluate. The resultant outcome of this similarity measure lies in the range of [0, 1]. The major drawback inferred by Saranya is that it provides high similarity between two vectors which have considerable difference in rating factor [Saranya and et. al. (2017)].
Similarity (c1, c2) = cosine (c1, c2) = .
|| ||^ ∗ || ||^ (1)
2.2.2 Pearson Correlation Coefficient (PCC)
• Pearson correlation quantifies how well two objects fit in a line. • Pearson correlation = 1, means that the two data are perfectly correlated • Pearson correlation = -1, means that the two data are not correlated at all
The formula to calculate the Pearson correlation is depicted by equation (2).
• Pearson correlation = ∑r.r' - ∑r ∑r'
( ∑ ∑ r)^2/N) (∑ ∑ )^2/N ) (2)
Where r and r’ are the ratings by the customer c1 and c2. 2.2.3 Euclidean Distance based Similarity
Euclidean based item similarity measures the degree of similarity between two points by measuring the distance between them.
• Euclidean distance for two points (x1, y1) and (x2, y2) is defined by equation (3).
• E.D.= 2 1 ^2 2 1 ^2 (3)
Where r1, r2, r3 . . . are the ratings by customer c1 And r1’, r2’, r3’ . . . are the ratings made by customer c2. 2.2.4 Tanimoto Coefficient based Similarity
• Tanimoto coefficient measures the similarity by measuring the overlap or the intersection between the two sets.
• Assuming two sets be A and B, then Tanimoto coefficient is defined by equation (4).
T (A, B) = N (A intersection B) / N (A) +N (B) - N (A intersection B) (4) • Where N (A intersection B) = number of elements that exist both in set A and B.
• N(A) = number of elements in set A • N(B) = number of elements in set B
Fig. 4. Tanimoto coefficient similarity
N (An intersection B) in the Fig. 4. expresses the number of items for which both user A and User B have shown interest. Whereas N (A) and N (B) express the items in which only User A and User B has shown interest respectively.
3. Proposed Method
This paper aims to present a new methodology to aid users in providing relevant items by filtering large databases on the basis of the intended feature. Unlike item based and user based collaborative filtering, the proposed method is based on rating the product differently on different aspects of products, i.e. on the basis of the different features of each product. A matching function is defined which measures the degree of similarity between two items or products. If a user U1 rates product P1 and user U2 rates product P2, then they can be considered similar if the difference between their respective ratings is in the range of {0,1 }. The function given below in equation (5) and (6) explains this concept in mathematical terms.
If, match (R1, R2) ∈ {range between 0 and 1} user U1 and user U2 are similar (5) If, match (R1, R2) ∉ {range between 0 and 1} user U1 and user U2 are dissimilar (6) Where R1 and R2 are the ratings given by a User U1 and User U2 on a “same feature”.
Considering an example as shown in the table 1 below, U1 and U2 have rated a particular product on four different features f1, f2, f3 and f4. By computing the difference between the ratings on each feature, match function gives whether the ratings are similar or dissimilar. And according to the match function, if the two ratings are similar then their products can be recommended to each other.
Table 1. An example to evaluate the match function
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4.3 Recommendation of Results based on the Euclidean Distance
The next step is locate the data point (say, p1) of the user for which recommendations are being searched. By employing the distance metrics of Euclidean distance measure from the present data point p1 to all the data points in the cluster in which it lies, the distances can be calculated. Nearer the data points, stronger is the similarity between them, and hence are more likely to be considered and be recommended.
for data point, p1 → f r1 , f r2 , f r3 , . . . f r n
for data point, p2 → f r1′ , f r2′ , f r3′ , . . . f r n′
where f1, f2, … fn represent the features and
r1, r2, … r n represent the ratings of f , f , … f for
and r1′, r2′, … r n′represent the ratings of f , f , … f for p2.
Applying the Euclidean distance formula from the equation (7), the distance between p1 and all the rest data points in a particular cluster can be formulated. The data points that are lesser in distances will be greater in similarity with the point p1 and hence will be recommended.
Euclidean distance = 1 1 2 2 3 3 . . . (7)
5. Future Scope and Concluding Results
The idea of the proposed method is to lessen up the cost of complex calculations used in the existing similarity measures. The existing systems tend to have complexity of O (MN), where M is the number of Users and N is the number of products which is quite large and scales from thousands to millions. By strongly analyzing the user based reviews on different movies (or products), it is possible to assess the distinctive features of the product. The different features can be further extracted by employing NLP techniques such as entity recognition, parts of speech tagging, etc. The nouns and pronouns basically represent different features of the product and the adjectives and adverbs represent the ratings of that particular feature. Thus by matching the products on the basis of each of its feature adds on a much more sense of meaning to the concept of similarity. To bring the idea into implementation, a feature based recommendation process is proposed which highlights the clustering and classification of data.
methodology aims to predict more accurate and faster results than the existing user based and item based recommendation systems.
6. References
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