当前位置:网站首页>[paper reading] ckan: collaborative knowledge aware autonomous network for adviser systems
[paper reading] ckan: collaborative knowledge aware autonomous network for adviser systems
2022-07-05 10:19:00 【Let me be quiet for a while】
original text :CKAN
Code :https://github.com/weberrr/CKAN
What: Put forward CKAN, One will Cooperative signal And Knowledge is connected The method of natural combination .
Why: Existing methods only focus on KG Medium Knowledge is connected (knowledge associations), Ignored Cooperative signal ( collaborative signals), This is often user-item Lacking in interaction .
How: Propose heterogeneous communication strategies , Encode two kinds of information naturally , Then the attention mechanism of knowledge perception is applied to distinguish the contributions of different knowledge-based neighbors .
Result:
Conclusion:
CKAN The previous method was not considered at the same time 【 Knowledge is connected 】 and 【 Cooperative signal 】 These two kinds of heterogeneous information , So try to combine the two kinds of information by natural coding .
Model
1. Heterogeneous Propagation
1.1 Collaborative communication
KG chart :
item-entity chart :
collaboration propagation
【user By interactive item To represent 、item By interactive user Interactive item To represent 】
【 The initial user said (initial entity set of user)】:( from user Interactive item To express )
among Yuv=1 Represent user u And objects v There is interaction ;e It's an entity ,(v,e) Indicates an item v And objects e There's a connection .
It means ,【 user 】 It is interacted by item, these item Related entities entity To represent the .【 Express with entity 】
Multiple users interact with the same item , Between them is 【 Users' collaborative neighbors 】.
Multiple items are interacted by the same user , Between them is 【 Collaborative neighbors of items 】.
Collaborative neighbors of items :
Vu Refers to the collaborative neighbors of items ,vu finger Vu This set Items in . Collaborative neighbors of items , namely item1,item2 All be user1 Interaction ,item2 yes item1 Objects of collaboration neighbors . Use entities related to collaborative neighbors entity To represent the .【 Express with entity 】
【 The initial item represents (initial entity set of item)】:( from item Interactive user Interaction item To express )
The objects are represented by their cooperative neighbors and their associated entities .
1.2 Knowledge map dissemination
Therefore, we can get after multi-level recursion user and item It means ,l-th Tail entity of , Aggregated (l-1)-th Head entity h Information about .
(user and item Multi level recursion Entity representation A general representation of )
Empathy , Get multi-level recursion Triples represent A general representation of :
【the knowledgebased high-order interaction information of user and item is successfully captured】
2. Knowledge-aware Attentive Embedding
The previous operation is represented by the entity of the object user, Express with the entity of neighbor items item, Collaborative communication is considered ; Then through multi-layer recursive propagation , Knowledge map propagation is considered , Get the fused head entity h And different relationships r Tail entity of t, But we also want to further distinguish the tail entities t The difference between , So add Attention mechanism To distinguish the tail entities with multi-layer information t.
Tailstock t Attention embeddedness indicates :( By the head entity h And relationship r control )
among ,
Call it attention factor , Its calculation process is as follows :
And pass softmax Normalize :
So you get l-th Representation of triples of :( It is divided into user and item, Here is the general expression )
You can see the calculation l-th When represented by triples of , Calculate the attention of each triplet in this layer , It is equivalent to aggregating all triples of this layer .
For the initial layer ( The first 0 layer ), hold user and item Corresponding entity Directly add to represent :
The author believes that the most central place is item Those entities that are directly related , And item It is closest in the buried layer , So add them to , To represent the origin:
So you get user and item Of additional representations:
3. Model Prediction
Each layer emphasizes different high-order connectivity and user preferences , Therefore, for the above 【user and item Of additional representations】 Aggregate separately , Adopted 3 Different ways :
Sum aggregator:
Pooling aggregator:
Concat aggregator:
Recently, I have arrived at the aggregation user Represents and item Indicates inner product :
Reference resources :
- CKAN Paper notes :https://zhuanlan.zhihu.com/p/181475023
attach :
Two pictures
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