Abstract: A non-soap detergent bar is provided, comprising: a detergent surfactant; a builder; a psyllium; a cellulose derivative; a filler, wherein the filler includes sodium silicate; and water; wherein the non-soap detergent bar is a solid.

Abstract: A soap bar is provided, comprising: a soap; water; a cellulose derivative selected from the group consisting of a hydroxyalkyl alkyl cellulose; and an additive selected from the group consisting of a psyllium, a sodium silicate and mixtures thereof; wherein the soap bar is a solid.

Abstract: The present invention provides dry mixes for skim coat mortars having improved pot life and workability while reducing cellulose ether dosage comprising a white cement, one or more fillers having a sieve average particle size of from 15 to 60 microns, from 0.25 to 0.5 wt. % of one or more gel-like crosslinked cellulose ethers containing polyether groups, preferably, a mixed cellulose ether having polyoxypropylene dioxyethylene ether crosslinks, and from 1 to 2.5 wt. % of one or more polymer redispersible powders (RDP). At least one gel-like crosslinked cellulose ether has a crossover point as measured by oscillation rheometry, at which storage modulus (G?) and loss modulus (G?) intersect and are identical, of 1.0? or less. The present invention also provides methods of using the dry mixes.

Abstract: A profile prediction system may identify one of multiple user profiles for a single computer. For example, a home computer may have multiple users that may not be targeted unless the user on the home computer can be identified. The system's user identification may be based on a clustering model that considers various browsing characteristics to identify different clusters that each correspond to a particular user or user profile. The model may be generated and refined by tracking web browsing over multiple sessions. Future activity on the computer may be used to identify which user is the source of the activity and the user may receive targeted content including advertisements.

Abstract: A method and a system are provided for deducing shadow user profile attributes for ad campaigns aimed at target users. In one example, the system extracts tagged data from source data. The tagged data includes label information associated with an actual profile for a user. The tagged data is associated with the user. The system prepares the tagged data by splitting the tagged data into datasets, including at least training data and test data. The system generates one or more individual models based on the tagged data, wherein the one or more individual models provide the ability to deduce attributes of a profile for the user. The system then generates a composite model based on the individual models. The composite model includes a combination of the individual models that are associated with the user. The system may charge a premium for ad campaigns that are aimed at target users who are each assigned one or more shadow profile attribute values.

Abstract: A profile prediction system may identify one of multiple user profiles for a single computer. For example, a home computer may have multiple users that may not be targeted unless the user on the home computer can be identified. The system's user identification may be based on a clustering model that considers various browsing characteristics to identify different clusters that each correspond to a particular user or user profile. The model may be generated and refined by tracking web browsing over multiple sessions. Future activity on the computer may be used to identify which user is the source of the activity and the user may receive targeted content including advertisements.

Abstract: A method, computer readable medium, and advertising server network for automatic internet advertising campaign variable optimization. The advertising server network comprises a module for receiving an advertising campaign specification containing a keyword template. The keyword template contains a plurality of keywords or bid phrases (which are selected to correspond to a particular business type). Then, based on one or more selected keywords, a module serves for collecting historical and/or forecasted keyword click data using a historical dataset. The keywords and click data are used as inputs into a linear programming optimization problem, which linear programming optimization technique is employed for calculating a bid amount for bidding on one or more display positions. The bid is optimized based an objective function subject to policy constraints. Given an optimized bid, a module places the bid using an auction server.

Abstract: A method and a system are provided for deducing shadow user profile attributes for ad campaigns aimed at target users. In one example, the system extracts tagged data from source data. The tagged data includes label information associated with an actual profile for a user. The tagged data is associated with the user. The system prepares the tagged data by splitting the tagged data into datasets, including at least training data and test data. The system generates one or more individual models based on the tagged data, wherein the one or more individual models provide the ability to deduce attributes of a profile for the user. The system then generates a composite model based on the individual models. The composite model includes a combination of the individual models that are associated with the user. The system may charge a premium for ad campaigns that are aimed at target users who are each assigned one or more shadow profile attribute values.