One of the things I am beginning to notice is the convergence in optimization technologies for ad targeting, content recommendations and product recommendations. In a prior article , I had talked about Context, Intent and Affinity as three techniques used to target ads on web pages and search results pages.
It turns out a number of products are beginning to use some combination of these approaches for content recommendations on content-oriented sites and product recommendations on eCommerce sites. In some cases, the same product can be applied to both content and product recommendations- Baynote being a prime example.
The table below summarizes what is out there. As you can see, these products use one or more of the following approaches to target content and products:
- context: use “aboutness” of content to recommend related content. Example: Sphere
- affinity/behavior: use click behavior of a given user or a cohort of users to cluster content and make recommendations. Example: Loomia, Baynote, Aggregate Knowledge etc.
- popularity: use current popularity of stories to recommend content. Example: Techmeme, One Spot, One Riot.
Some untapped opportunities:
- Using twitter and facebook status to recommend content/products
- Using tweats to gauge break out topics. Would nicely augment products like techmeme.
- Providing video and image recommendations.
| Product | Focus | Approach | Business Model |
| Aggregate Knowledge | Content Recommendations based on click-behavior | Works very similar to Amazon product recommendations. E.g. people who clicked on this story A also clicked on Story B, C, D. | |
| Baynote | Content and Product Recommendations based on click-behavior | Works very similar to Amazon product recommendations. E.g. people who clicked on this story A also clicked on Story B, C, D. | |
| Loomia | Content Recommendation based on click-behavior | Works very similar to Amazon product recommendations. E.g. people who clicked on this story A also clicked on Story B, C, D. Also offers video recommendations. | |
| One Riot | Search results driven by real-time popularity of items | Collects data from browser beacons to gauge popularity of stories and use it to rank stories | |
| One Spot | Content Recommendation- based on story popularity | Uses linking behavior between sites to rank and recommend popular stories on a given topic. Works similar to Google Page Rank. | $150/month per topic |
| Outbrain | Content Recommendations | ||
| Sphere | Content recommendations based on topic | ||
| Taboola | Video recommendations | ||
| Techmeme | Content Recommendation for Technology topics- based on story popularity | Seems to be surface most popular articles on technology at a given time. I strongly suspect that it uses linking behavior between sites to guess popularity of a given story. | Sponsored links priced at $5 CPM (actual pricing is several thousand dollars per month which translates to about $5 CPM at current page views) |
