1) The document discusses recommender system algorithms and architecture. It covers common recommendation techniques like collaborative filtering, content-based filtering, and graph-based recommendations.
2) It also discusses challenges like cold starts for new users and items. For new users, it recommends using demographic data or initial feedback to understand interests. For new items, it suggests using content information.
3) The document proposes a feature-based recommendation framework that connects users, items, and their features to address challenges like cold starts and providing explanations. This framework represents users and items in a feature space to make recommendations.
This document summarizes the success of Taobao.com, an ecommerce platform in China similar to eBay and Amazon. It discusses how Taobao overtook eBay in China by offering free listings, innovative customer service, and diversifying its ecosystem. Some key lessons from Taobao's success include keeping up with industry trends, creating quality and fresh content, improving customer service, and focusing on company promotion.
1) The document discusses recommender system algorithms and architecture. It covers common recommendation techniques like collaborative filtering, content-based filtering, and graph-based recommendations.
2) It also discusses challenges like cold starts for new users and items. For new users, it recommends using demographic data or initial feedback to understand interests. For new items, it suggests using content information.
3) The document proposes a feature-based recommendation framework that connects users, items, and their features to address challenges like cold starts and providing explanations. This framework represents users and items in a feature space to make recommendations.
This document summarizes the success of Taobao.com, an ecommerce platform in China similar to eBay and Amazon. It discusses how Taobao overtook eBay in China by offering free listings, innovative customer service, and diversifying its ecosystem. Some key lessons from Taobao's success include keeping up with industry trends, creating quality and fresh content, improving customer service, and focusing on company promotion.
「活用您的Big Data,實現線上服務行銷的精準推薦」
5.24 @ 六福皇宮 13:30-14:10 - Track: Big Data for Cloud Service
主講者:陳育杰 / Etu 資深協理
《議題簡介》
在這個資訊氾濫的時代,每個人都希望可以只接收或看到自己感興趣的內容,不論是新聞、商品訊息、甚至是廣告。也因此,對於所有的企業來說,如何針對你的客戶做到更精準的推薦,變得是一個越來越重要且無可避免的一個課題,更正確的說,精準行銷的核心正是來自於精準的...... 推薦。Amazon 的推薦機制(Recommendation)對於新客戶轉化率的提升與舊客戶每筆訂單金額的提高,一直是所有電子商務公司的一個典範。而精準推薦並不是只可以用在線上的服務,今天不論是虛擬或實體的通路,如何隨時提供客戶感興趣的推薦清單,以維持客戶忠誠度並提高銷售金額,都是企業成長獲利的一大關鍵。在這個演講當中,Etu 團隊將為你介紹如何運用 Big Data 處理與分析的技術,讓企業可以很方便的來分析線上與實體的客戶和商品的購買或瀏覽的關聯性,並輕易地建構出對客戶有效的推薦清單。
The ppt is about how to use Refine search improve longtailed product sale and user experience. It is writed in Chinese, if you need English version, please contact us directly.
Get insights about auto products and consumers from users' feedback in social media, to improve operation, social marketing and product. 用来自社交媒体的用户反馈来了解汽车和用户,提高运营、社交营销和产品设计。
「活用您的Big Data,實現線上服務行銷的精準推薦」
5.24 @ 六福皇宮 13:30-14:10 - Track: Big Data for Cloud Service
主講者:陳育杰 / Etu 資深協理
《議題簡介》
在這個資訊氾濫的時代,每個人都希望可以只接收或看到自己感興趣的內容,不論是新聞、商品訊息、甚至是廣告。也因此,對於所有的企業來說,如何針對你的客戶做到更精準的推薦,變得是一個越來越重要且無可避免的一個課題,更正確的說,精準行銷的核心正是來自於精準的...... 推薦。Amazon 的推薦機制(Recommendation)對於新客戶轉化率的提升與舊客戶每筆訂單金額的提高,一直是所有電子商務公司的一個典範。而精準推薦並不是只可以用在線上的服務,今天不論是虛擬或實體的通路,如何隨時提供客戶感興趣的推薦清單,以維持客戶忠誠度並提高銷售金額,都是企業成長獲利的一大關鍵。在這個演講當中,Etu 團隊將為你介紹如何運用 Big Data 處理與分析的技術,讓企業可以很方便的來分析線上與實體的客戶和商品的購買或瀏覽的關聯性,並輕易地建構出對客戶有效的推薦清單。
The ppt is about how to use Refine search improve longtailed product sale and user experience. It is writed in Chinese, if you need English version, please contact us directly.
Get insights about auto products and consumers from users' feedback in social media, to improve operation, social marketing and product. 用来自社交媒体的用户反馈来了解汽车和用户,提高运营、社交营销和产品设计。
随着Ruby on Rails的流行,Ruby正在Web开发领域体现出它独特的魅力,和其他语言相比选择Ruby有什么优势?Ruby的运行效率高吗?Ruby能用来做网游充值这样“Mission Critical”的事情吗?Ruby能用来开发游戏吗?Ruby程序员难找吗?本主题将会和大家分享在网游运营平台开发的一些心得。
The document discusses techniques for improving website performance, including:
1. Focusing on front-end optimizations as they account for 80-90% of response time.
2. Following the 80/20 rule - optimizing the 20% of code that affects 80% of response time like assets on the front-end.
3. Using techniques like image sprites, combined scripts and stylesheets, CDNs, caching, gzip compression, and reducing cookie sizes and HTTP requests to improve response times.
4. 推荐系统定义
— 维基百科:form
or
work
from
a
specific
type
of
information
filtering
system
technique
that
attempts
to
recommend
information
items
(item,
music,
books,
news,
images
etc.)
or
social
elements
(e.g.
people,
events
or
groups)
that
are
likely
to
be
of
interest
to
the
user.
28. 推荐算法
— 关联规则:a
method
for
discovering
interesting
relations
between
variables
in
large
databases
支持度
support(A ⇒ B)=P(A ∪ B)
置信度 confidence(A ⇒ B)=P(B|A)