SEKE 2012 Proceedings - Knowledge Systems Institute

Recommender systems are usually classified into two
categories. The first category is content-based recommendations
[2], and the second category is collaborative filtering
recommendations [7]. Content-based methods utilize
the content information of items to generate recommendations.
Content information means the intrinsic features
of items. In movie recommendation scenario, the content
information of a movie is mainly its title, director, actors,
genre, plot summary, keywords etc. The first step
of content-based methods is to figure out the commonalities
among the movies user has given high rating. Then
the system will generate the user’s preference with these
commonalities, and the recommended movies will be apparently
those ones similar to the user’s preference. Collaborative
filtering methods utilize users’ rating information to
generate recommendation. According to the way of using
the rating information, collaborative filtering methods can
be classified into two categories: Memory-based CF and
Model-based CF. Memory-based CF directly makes use of
entire or part of rating information to generate recommendation.
Model-based CF do not directly use rating information
to generate recommendation, these methods first use
rating information to train a model, which is then used to
make recommendation.
3. Experimental Method
The E-commerce site used in this paper has been one of
the most popular online bag retailers, and most of its customers
are from North America. We collaborate with the
site for over half a year to gather real data from its site.
3.1. Data Set
The data was gathered during January 2011 to June
2011. This data set contains more than 363000 times visits
from over 63000 users.We get bag features from the site’s
database directly, these features to describe bags are color,
size, price, discount, type, brand, etc. The feature database
contains about 1300 bags. About 70 percent of the users
are new user, and they come to visit our site for the first
time and on average every user visits only about 5 product
pages.
3.2. Evaluated Algorithm
We explore four categories of recommendation methods.
The first category is content-based method [2]. And the
second category is collaborative filtering method, this category
contains two methods. One is item-to-item collaborative
filtering method [5], and the other is traditional userbased
collaborative filtering method [4]. The third category
is method based on simple statistical result, here we use the
simplest most popular visited N products. The last category
is about some normal business sense, we select the cheapest
and newest products to recommend to users.
Content-based: Content-based recommendation system
tries to find the products that are similar to the items which
user liked in the past. We recommend the products most
similar to current visited product. Similarity between product
a and b is calculated as follow:
similarity a,b = |F a ∩ F b |
|F a ∪ F b |
where F a is the feature set of product a, and F b is the
feature set of product b. |F a ∩ F b | denotes number of features
product a and b both have, and |F a ∪ F b | denotes the
entire number of features product a and b have.
Item-to-item-CF: Item-to-item collaborative filtering
method defines similarity between items by the tendency
of users often visit or purchase these items together. We
apply the method similar to the algorithm proposed in [5].
Similarity between product a and b is calculated as follow:
similarity a,b =
CV (a, b)
V (a)+V (b)
where CV(a,b) denotes times of products a and b are covisited,
V(a) is the times of product a has been visited and
V(b) is the times of product b has been visited.
User-based-CF: User-based-CF method first figure out
similar users of the target user. And then generate recommendation
result to target user according to her similar
users’ historical preference. Generally, we recommend
products visited most often by target user’s similar users.
Because we have no rating information in the site, we replace
rating by times of visit to products in user vectors.
The similarity between two user vectors can be measured
by Pearson correlation.
Most-popular: Most-popular method may be the most
widely used strategy to recommend products without personalization.
We recommend most popular visited products
to users. This method does not need complex computation
and get a relatively good performance at a very low cost.
Cheapest: Cheapest method is simple, we choose the
cheapest products to recommend. One important feature of
E-commerce is online shops can reduce fees compared to
bricks-and-mortar stores so that they can provide a lower
price. The performance of cheapest method will tell us
whether low price is one incentive for users to shop online.
Newest: Just as simple as Cheapest method, we recommend
the newest products to users. New products will arrive
on shelves at online shop earlier than that in bricks-andmortar
store, so this method will detect whether a significant
percent of consumers shop online for this reason.
(1)
(2)
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