Analyzing shopping behavior with Self Identity

For each sequence data we generated above, we calculate the number of item the customer viewed or purchased as #v, #p, the number of each at- tribute #Av, #Ap has been viewed or purchased, the total attribute for each

image#aimage. Then we produce a weighted attribute hit rate functionhri =

(#Ai_v + w ∗ #Ai_p)/(#v + w ∗ #p) ∗ #aimage where w is a fixed weight

to enhance the influence of purchase, i refers to the index for each attribute. We then defined the distance function dhri,hrj for hit rate the same as equa-

tion 5.13. The distance is scaled to 0,1 and the smaller the dhri,hrj is, the more

dissimilar between hriandhrj.

The hit rate function collects the distribution of attributes during online shopping, it can be considered as a attribute level behavior feature for each user. We then apply statistic methods such as average, variance to get the feature of distribution and use clustering to split user behavior to different

groups. With the theory of Self Identity, we can define each group from Har- monious to Fragmented by analyzing the distribution. In details, we firstly rerank the hr from high to low which ignore the attribute information, and use K-means to cluster the reranked hr into five groups which refers to Harmo- nious, Mildly Conflicted, Vulnerable, Disturbed and Fragmented respectively. In cluster model, we then analyze the results in each cluster to discuss whether Self Identity can be used to describe customer behavior. In our assumption, the user with harmonious self identity tends to view or purchase products with specific attributes, therefore, the data in harmonic cluster have a larger vari- ance and a smaller change to view more typies of attributes. On the contrary, the fragmented behaviors can be also described as smaller variance and larger view range for attributes. In detail, those feature can be described by aver- age variance V ar(X) = 1_nP|X|

i=1(xi − ¯x)2, the number of attribute that never

viewed |A||∀P (A) = 0 and the number of attribute that viewed more than half chance |A||∀P (A) > 0.5. In experiment, we will prove Self Identity can be used to distinguish customer behaviors by the three statistical results.

In addition, the previous research of Self Identity describes that when identity is activated, a feedback loop is also established and environmental factors will result in behaviors, meanwhile, the behavior can affect the envi- ronmental factors and finally reflect to Self Identity (Stets & Burke, 2003). The former identity behavior is analyzed by clustering the reranked hit ra- tio data to prove Self Identity can affect shopping behaviors, while, the lat- ter reflection still need to be investigated. In online shopping environment, the reflection process can be described as the attribute preference changing over viewing different products. In other words, the different attributes rec- ommended by RS will result in Self Identity behavior changing, and those changing may result in different level of pruchase intention.

Both harmonic and fragmented behaviors are easy to be explained by sta- tistical data and those behaviors are more stable than mid behaviors. To an- alyze the reflection process of Self Identity, we choose the mid behavior be- tween harmonic and fragmented as the datasource. In details, we select mid behaviors with longer duration and split their shopping behavior with small sliding window to determine the attribute based Self Identity changing over shopping. There are three different kinds of data can be found as following.

From harmonious to fragment The three variables, average variance, num- ber of never viewed attributes and number of usually viewed attribute,

change from harmonious level to fragment level which means the cus- tomer firstly concentrate on specific items, but changed to uncertain products during Shopping. This process can be described by two dif- ferent situations. The fisrt one is a customer prefer to specific attribute features but be attracted by other attributes during shopping. The sec- ond one is a customer focus on a specific products and purchase them, then he is attracted by other different products.

From fragment to harmonious The three variables described above change from fragment level to harmonious level. This process describes the customer find their preferred attribute combination during shopping and usually result in high purchase intension.

Keep mildly We can not find statistical relationship from the variables de- scribed above. That may because the customer keep changing perfer- ence during shopping or the attributes we used are not enough to de- scribe this kind of user.

As harmonious behavior usually result in high purchase intention, to de- velop different strategies for different mid behaivor customer is necessary. Specially, for users chaging from fragment to harmonious, we prefer to en- rich the attribute range predicted by RS to accurate this process and lead to higher purchase rate.

In experiment, to get longer analyzing data, we increase the time weight wt in score function 5.14 and get high score data. Then we apply sliding

window to generate time series data. Finally, we use the Self Identity cluster model to analyse the changing of behaviors for mid behavior custoemr.