有个新系统或是新技术，如何去作推荐呢，下面的文章也许会给我们一些思路。
http://www.readwriteweb.com/archives/recommender_systems.php
A Guide to Recommender Systems
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. He identified that Pandora used "deep structural analysis of an item" for its recommendations, Strands focused on "intensive social behavior analysis around an item" and Aggregate Knowledge did "structural analysis of an item, paired with behavioral analysis around the item".
A couple of years ago, Alex Iskold outlined what he saw as the 4 main approaches to recommendations:
- Personalized recommendation - recommend things based on the individual's past behavior
- Social recommendation - recommend things based on the past behavior of similar users
- Item recommendation - recommend things based on the item itself
- A combination of the three approaches above
Amazon: King of Recommendations
In that post, Alex analyzed what Amazon.com - probably the canonical example of recommendations technology on the Web - used to power it's recommendations. Unsurprisingly, he found that Amazon used all 3 approaches (personalized, social and item). Amazon's system is very sophisticated, but at heart all of its recommendations "are based on individual behavior, plus either the item itself or behavior of other people on Amazon." What's more, the aim of it all is to get you to add more things to your shopping cart.
As Xavier identified, other newer Internet companies have tended to focus on specific methods of recommendation. For Pandora, it is a deeper analysis of the item (using its "gene" theory); Strands has taken a boatload of VC money to try and become the number 1 social recommendations provider on the planet; and Aggregate Knowledge is taking more of a behavioral approach.
Google: Focus on Personalized Recommendations
The most successful Internet company of this era has without a doubt been Google. It too has been using recommendation technologies to improve its core search product. There are two ways that Google does this:
- Google customizes your search results "when possible" based on your location and/or recent search activity;
- When you're signed in to your Google Account, you "may see even more relevant, useful results based on your web history."
So Google is using both your location and your personal search history to make its search results supposedly stronger. This is very much the 'personalized recommendation' approach - and indeed personalization has been a buzzword for Google in recent years. However, the two other types of recommendation are also present in Google's core search product:
- Google's search algorithm PageRank is basically dependent on social recommendations - i.e. who links to a webpage;
- Google also does item recommendations with its "Did you mean" feature.
There are surely other ways recommendations technologies are being deployed in Google search - not to mention the range of other products Google has. Google News, its start page iGoogle, and its ecommerce site Froogle all have recommendation features.
ReadWriteWeb Resources for Recommendation Technologies
Let us know what types of recommendation technologies other companies are using. We also invite you to explore using our custom ReadWriteWeb Resources:
- RWW Recommendation Industry Feed Favorites OPML file (save link)
- RWW Recommendation Industry Feeds - Best of Feed (copy and paste to your reader)
- Click to preview the above feeds before subscribing (pop-up window)
- RWW Recommendation Site Search (Visit and Bookmark)
http://www.readwriteweb.com/archives/5_problems_of_recommender_systems.php
5 Problems of Recommender Systems
Earlier this week we posted a Guide to Recommender Systems, as part of our series on recommendation technologies. In this post we look at some of the challenges in building or deploying a recommender system. And yes, Napoleon Dynamite is one of them.
This week an event called Recked was held in Amsterdam, aimed at engineers interested in these systems. The event was hosted by Wakoopa and Strands (we've embedded the presentations below). In those presentations, there were some hints at the problems that these companies have to overcome to build an effective recommender system.
1. Lack of Data
Perhaps the biggest issue facing recommender systems is that they need a lot of data to effectively make recommendations. It's no coincidence that the companies most identified with having excellent recommendations are those with a lot of consumer user data: Google, Amazon, Netflix, Last.fm. As illustrated in the slide below from Strands' presentation at Recked, a good recommender system firstly needs item data (from a catalog or other form), then it must capture and analyze user data (behavioral events), and then the magic algorithm does its work. The more item and user data a recommender system has to work with, the stronger the chances of getting good recommendations. But it can be a chicken and egg problem - to get good recommendations, you need a lot of users, so you can get a lot of data for the recommendations.
2. Changing Data
This issue was pointed out in ReadWriteWeb's comments by Paul Edmunds, CEO of 'intelligent recommendations' company Clicktorch. Paul commented that systems are usually "biased towards the old and have difficulty showing new".
An example of this was blogged by David Reinke of StyleHop, a resource and community for fashion enthusiasts. David noted that "past behavior [of users] is not a good tool because the trends are always changing" (emphasis ours). Clearly an algorithmic approach will find it difficult if not impossible to keep up with fashion trends. Most fashion-challenged people - I fall into that category - rely on trusted fashion-conscious friends and family to recommend new clothes to them.
David Reinke went on to say that "item recommendations don't work because there are simply too many product attributes in fashion and each attribute (think fit, price, color, style, fabric, brand, etc) has a different level of importance at different times for the same consumer." He did point out though that social recommenders may be able to 'solve' this problem.
3. Changing User Preferences
Again suggested by Paul Edmunds, the issue here is that while today I have a particular intention when browsing e.g. Amazon - tomorrow I might have a different intention. A classic example is that one day I will be browsing Amazon for new books for myself, but the next day I'll be on Amazon searching for a birthday present for my sister (actually I got her a gift card, but that's beside the point).
On the topic of user preferences, recommender systems may also incorrectly label users - a la this classic Wall St Journal story from 2002, If TiVo Thinks You Are Gay, Here's How to Set It Straight.
4. Unpredictable Items
In our post on the Netflix Prize, about the $1 Million prize offered by Netflix for a third party to deliver a collaborative filtering algorithm that will improve Netflix's own recommendations algorithm by 10%, we noted that there was an issue with eccentric movies. The type of movie that people either love or hate, such as Napoleon Dynamite. These type of items are difficult to make recommendations on, because the user reaction to them tends to be diverse and unpredictable.
Music is full of these items. Would you have guessed that this author is a fan of both Metallica and The Carpenters? I doubt Last.fm would make that recommendation.
5. This Stuff is Complex!
We're stating the obvious here, but the below slide from Strands' presentation at Recked illustrates that it takes a lot of variables to do even the simplest recommendations (and we imagine the below variables only scratch the surface):
So far only a handful of companies have really gotten recommendations to a high level of user satisfaction - Amazon, Netflix (although of course they are looking for a 10% improvement on their algorithm), Google are some names that spring to mind. But for those select few success stories, there are hundreds of other websites and apps that are still struggling to find the magic formula for recommending new products or content to their users. Indeed we at ReadWriteWeb would love to get readers clicking around our site more to discover other content, and we've tried several plug-ins and methods to achieve this - but we're not satisfied yet.
What Have We Missed?
There are many other issues that can happen with recommender systems - some offer up too many 'lowest common denominator' recommendations, some don't support The Long Tail enough and just recommend obvious items, outliers can be a problem, and so on.
Let us know your thoughts and what other problems recommendation technologies face as they continue to ramp up.
ReadWriteWeb Resources for Recommendation Technologies
We also invite you to explore using our custom ReadWriteWeb Resources:
- RWW Recommendation Industry Feed Favorites OPML file (save link)
- RWW Recommendation Industry Feeds - Best of Feed (copy and paste to your reader)
- Click to preview the above feeds before subscribing (pop-up window)
- RWW Recommendation Site Search (Visit and Bookmark)
Also, we've embedded the three presentations (Strands, Wakoopa and Reccoon) from Recked below: