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We're running a special series on recommendation technologies and in this post we look at the different approaches - including a look at how Amazon and Google use recommendations. The Wikipedia entry defines "recommender systems" as "a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages, etc.) that are likely of interest to the user." That entry goes on to note that recommendations are generally based on an "information item (the content-based approach) or the user's social environment (the collaborative filtering approach)." We think there's also a personalization approach, which Google in particular is focused on. We explore some of these concepts below. In a recent post, Xavier Vespa of the blog HyveUp analyzed 3 different approaches to recommendation engines on the Web. A Guide to Recommender Systems - ReadWriteWeb A Guide to Recommender Systems - ReadWriteWeb
Ladies and gentlemen… In this post I proudly present the Top 100 of Best Software Engineering Books, Ever. I have created this list using four different criteria: 1) number of Amazon reviews, 2) average Amazon rating, 3) number of Google hits and 4) Jolt awards. Please refer to the bottom of this post to find out how I performed the calculations, how to receive the full top 100 list in PDF MS Word, and why that obscure and silly little publication of yours has not made it on my list. 1 Steve McConnellCode Complete: A Practical Handbook of Software Construction 2 Elisabeth Freeman, etc.Head First Design Patterns Managing Software Development: Top 100 Best Software Engineering Managing Software Development: Top 100 Best Software Engineering