Thu. : AI
Fri. : complexity , OS .
Thu. : AI
Fri. : complexity , OS .
在這8天內 是否能成為勇者 就要看我怎麼利用時間了
也想順便回家 依然還是很想家 …
就像孩提時代的我 與家人烤肉 吃柚子 看煙火
最大的收穫 莫過於認識了一些博班的朋友 他們也都是我的長輩
分別是 AI、Complexity、OS 這三科
老實說 暑假也沒很認真去念這三科 離考試時間也只剩下2星期不到
或許往後在業界待悶了 又懷念起學術界的自由時 我就會再度回來
整個就是溫暖 溫暖 好溫暖
而我卻不能說聲 : 爸爸 媽媽 你們去休息就好 我已經可以賺錢了
我希望 家人能盡快退休休息 去享受一下這世界的美麗
這24年來家人每天不間斷的工作 看在我心裡 實在感觸良多
隨著自己的年紀變大 煩腦就逐漸變多 我已不是懵懵懂懂的小朋友了
我希望 我能盡快到業界去上班 在社會好好闖蕩
我希望 我能每天感受到父母親的用心 父母親的辛勞
從以前到現在 我努力的動機 就是為了要讓家人過好一點的生活
我要貫徹我的理念 加油 ： 別忘了自己的家人 ！
Progress report (8/17 ~ 9/21)
Written by Yu-Chung Shen
After my presentation at the group meeting recently, I’ve considered my research topic for a period of time. The research I would like to do is about information searching and recommendation. Here the information I means are the technical papers, and therefore I will use them as the experimental data for my research. The system requirements I proposed can be listed as the following:
1. The overall system structure is built on the distributed environment. It means that each user has an agent to help them manage their technical papers for further sharing and recommendation.
2. Each agent has to communicate with its acquaintance agents so that they can exchange and share repository.
3. When some user give a query such as finding some topic (e.g. recommendation systems), their agents must automatically propagate the query to those acquaintance agents that have the high information provision ability.
4. Each agent maintains a profile which describes the interests of its acquaintances. Based on the profile, each agent can recommend items to the acquaintances who are interested in them.
I think finding the right agents in a large and dynamic network to provide the needed information in a timely fashion is an ambitious task. I would like to propose a method which enables agents to search effectively, furthermore, to identify their acquaintances’ interests and then actively make recommendations for them. The following briefly describe some of the implementation details I’ve considered for now.
1. First, when some user gives a query, his agent must propagate the query to their acquaintances that are most likely to provide the answers. Here is some method to define the information provision ability. (1) Item Vector method: we can define item vectors for each agent’s repository and use cosine similarity measurement to calculate the interest similarity between two agents. When some user gives a query, his agent can propagate the query to the more similar agents, that is, the more similar means the more high information provision ability (2) Content-based method: we can use the well known TF-IDF technique to construct a vector for a query and for each agent’s repository, then to calculate the similarity between a query and a user’s repository. We finally propagate the query to the agents which have high similarity. (3) Routing indices method: each agent maintains a global ontology that represents category of information (Here I mean the technical papers). When some user give a query which want to retrieve information of a specific category, his agent will propagate the query to the agents who have much more information of that specific category or similar category based on the ontology previously defined.
2. Second, in order to provide information recommendation capability, each agent should maintain an interest profile for some of their acquaintances. Agents construct profiles when they receive queries. For example, when agent A receives a query from agent B, it is obviously that agent B is interested in the query now, so we can update agent A’s profile for agent B to record that agent B have some probability to be interested in the query. Next time when agent A receives the same query again from agent B, agent A must also update the profile for agent B to add more probability to imply that agent B is more likely interested in the query. When the probability exceed some predefine value, agent A will automatically recommend items to agent B, which the items are similar to the query that agent B have requested before.
The system described above exploits users’ queries to derive their interests for the recommendation purpose, and it use some of the information provision ability measurement to route the queries to the right agents. To sum up briefly, I want to design agents that can effectively route the queries to the right agents and have the learning ability to learn each user’s interests profile to actively make recommendations.
為了趕 progress report 又在實驗室待到好晚
看來這地方可以待 : )